precipitation field experiment

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Guidelines and considerations for designing field experiments simulating precipitation extremes in forest ecosystems

Revised: August 20, 2019 | Published: December 3, 2018

• Research Article

Warming and altered precipitation independently and interactively suppress alpine soil microbial growth in a decadal-long experiment

• Shengjing Jiang
• Qirong Shen
• Zhibiao Nan
• State Key Laboratory of Herbage Improvement and Grassland Agro-Ecosystems, College of Pastoral Agriculture Science and Technology, Lanzhou University, China ;
• Jiangsu Provincial Key Lab for Solid Organic Waste Utilization, Jiangsu Collaborative Innovation Center for Solid Organic Waste Resource Utilization, Nanjing Agricultural University, China ;
• Institute of Ecology, College of Urban and Environmental Sciences, and Key Laboratory for Earth Surface Processes of the Ministry of Education, Peking University, China ;
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• Jin-Sheng He
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eLife assessment

Introduction, materials and methods, data availability, article and author information.

Warming and precipitation anomalies affect terrestrial carbon balance partly through altering microbial eco-physiological processes (e.g., growth and death) in soil. However, little is known about how such processes responds to simultaneous regime shifts in temperature and precipitation. We used the 18 O-water quantitative stable isotope probing approach to estimate bacterial growth in alpine meadow soils of the Tibetan Plateau after a decade of warming and altered precipitation manipulation. Our results showed that the growth of major taxa was suppressed by the single and combined effects of temperature and precipitation, eliciting 40–90% of growth reduction of whole community. The antagonistic interactions of warming and altered precipitation on population growth were common (~70% taxa), represented by the weak antagonistic interactions of warming and drought, and the neutralizing effects of warming and wet. The members in Solirubrobacter and Pseudonocardia genera had high growth rates under changed climate regimes. These results are important to understand and predict the soil microbial dynamics in alpine meadow ecosystems suffering from multiple climate change factors.

This important study addresses the long-term effect of warming and precipitation on microbial growth, as a proxy for understanding the impact of global warming. The evidence that warming and altered precipitation exhibit antagonistic effects on bacterial growth is compelling and advances our understanding of microbial dynamics affected by environmental factors. This study will interest microbial ecologists, microbiologists, and scientists generally concerned with climate change.

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Global climate change is threatening multi-dimensional ecosystem services, such as terrestrial primary productivity and soil carbon storage ( Jansson and Hofmockel, 2020 ; Walker et al., 2022 ; Zhou et al., 2022 ), especially in high-elevation ecosystems ( Ma et al., 2017 ; Liu et al., 2018 ). Of these, the effects of global climate change on microbial processes related to soil carbon cycling should receive more extensive attention, because carbon balance will have feedbacks on climate system, and further reinforce/diminish the net impact on ecosystem functioning ( Jansson and Hofmockel, 2020 ). Microbial growth and death, the critical eco-physiological processes, serve as the major engine of soil organic carbon (SOC) turnover and thus dominates the feedback on climate ( Sokol et al., 2022 ). Quantitative estimates of trait-based responses of microbes to multiple climate factors is critical for improved biogeochemical models and predicting the feedback effects to global change.

Climate warming and precipitation regime shift can influence soil microbial physiological activities directly or indirectly ( Schimel, 2018 ; Jansson and Hofmockel, 2020 ; Purcell et al., 2022 ; Sokol et al., 2022 ). The Tibetan Plateau is considered among the most sensitive ecosystems to climate change ( Liu et al., 2018 ). In such alpine regions, warming can alleviate low temperature limitations to enzymatic activity, stimulating SOC mineralization and microbial respiration ( Dieleman et al., 2012 ; Streit et al., 2014 ). Long-term warming reduces soil organic carbon pools and exacerbates carbon limitation of soil microbes, causing a negative effect on microbial growth and eco-physiological functions ( Jansson and Hofmockel, 2020 ; Melillo et al., 2017 ; Purcell et al., 2022 ; Streit et al., 2014 ). Precipitation fluctuation constrains microbial physiological performance and functions, which is expected to be the major consequence of future climate change in mesic grassland ecosystems ( Cook et al., 2015 ; McHugh et al., 2017 ; Oppenheimer-Shaanan et al., 2022 ; Yuan et al., 2017 ). Reduced precipitation affects soil processes notably by directly stressing soil organisms, and also altering the supply of substrates to microbes via dissolution, diffusion, and transport ( Schimel, 2018 ). Increased frequency and magnitude of precipitation events could cause microbial species loss by ‘filtering out’ the taxa with low tolerance to increased soil moisture and drying-rewetting ( Evans and Wallenstein, 2014 ). In addition, higher mean annual precipitation (MAP) triggers an increase in SOC decomposition ( Zhou et al., 2022 ), which could cause a negative effect on microbial growth in long term. Collectively, climate change typically causes negative consequences on the microbe-associated processes in terrestrial ecosystems.

As temperature and precipitation are of particular relevance, the interactive effects of warming and altered precipitation remain largely illusive, especially on the population growth of soil microbes ( Zhu et al., 2016 ; Song et al., 2019 ). Drought limits the resistance of the entire system to warming ( Hoeppner and Dukes, 2012 ). Higher evapotranspiration in a warmer world will result in chronically lower average soil moisture ( Reich et al., 2018 ), further reducing the eco-physiological performance of soil microbes ( Schimel, 2018 ). In contrast, enhanced precipitation relieves overall water limitations caused by warming and improved primary productivity and soil respiration ( Fay et al., 2008 ). The responses of microbial population growth to multiple climate factors could be complex because (i) the changed climate conditions can directly affect the eco-physiological characteristics of soil microbes and (ii) indirectly affect microbial functioning by altering soil physicochemical properties (e.g. redox conditions and nutrient allocation) and aboveground plant composition ( Qi et al., 2022 ; Yang et al., 2021 ). The response of decomposer growth rates to the interaction of climate factors may be strongly idiosyncratic, varying among taxa, thus making predictions at the ecosystem level difficult.

The goal of current study is to comprehensively estimate taxon-specific growth responses of soil bacteria following a decade of warming and altered precipitation manipulation on the alpine grassland of the Tibetan Plateau, by using the 18 O-quantitative stable isotope probing ( 18 O-qSIP) ( Figure 1A ). We focused on the single and interactive effects of temperature (T) and precipitation (P) on the population-specific growth of soil bacteria. We classified the interaction types as additive, synergistic, weak antagonistic, strong antagonistic and neutralizing interactions between climate factors ( Figure 1B ) by using the effect sizes and Hedges’ d (an estimate of the standardized mean difference; Côté et al., 2016 ; Harpole et al., 2011 ; Ma et al., 2019 ; Yue et al., 2017 ). We addressed the following hypotheses: (1) long-term warming and altered precipitation regimes (i.e. drought or wet) have negative effects on microbial growth in alpine meadow soils; (2) the interactive effects between warming and altered precipitation on microbial population growth rates are not simply additive.

Field experiment design for simulated warming and altered precipitation, qSIP incubation, and the growth responses of soil bacteria to changing climate regimes.

To examine the effects of warming and altered precipitation on an alpine grassland ecosystem, two levels of temperature (T 0 , T + ), and three levels of precipitation (-P, nP, +P) were established in 2011. The soil samples were collected in 2020 and used for 18 O-qSIP incubation ( A ). Potential interaction types between multiple climate factors ( B ). The diagram shows that two factors (X and Y) of warming and altered precipitation impact a biological response in the same direction (upper) or have opposing effects on when acting separately. Their combined effect could be additive, that is the sum of the two factor effects. Alternatively, the interaction types can be antagonistic or synergistic. Null model (we use the additive expectation as the null model here) provides the threshold for distinguishing between these interactions.

Overall growth response of soil bacteria to warming and altered precipitation

Excess atom fraction 18 O value ( Figure 2 ) and the population growth rate of each OTU were calculated using the qSIP pipeline. Collectively, 1373 OTUs were identified as ‘ 18 O incorporators’ (i.e. OTUs with growth rates significantly greater than zero) and used for subsequent data analyses. The maximum cumulative growth rates of the whole communities occurred in the ambient temperature and ambient precipitation condition (T 0 nP), and all climate manipulations had negative effects on soil bacterial growth ( Figure 3A ). The individual impact of warming, drought, and wet conditions resulted in the most substantial negative effects on bacterial growth compared with the combined effects of warming × drought and warming × wet. A result that illustrates the antagonistic interactions between warming and modified precipitations patterns ( Figure 3B ). Moreover, the combined effect size of wet and warming was smaller than that of drought and warming, indicating a higher degree of antagonism of warming × wet.

Species-specific shifts of 18 O excess atom fraction (EAF- 18 O).

Bars represent 95% confidence intervals (CIs) of OTUs. Each circle represents an OTU and color indicates phylum. The open circles with gray bars represent OTUs with 95% CI intersected with zero (indicating no significant 18 O enrichment); Closed circles represent the OTUs enriched 18 O significantly, whose 95% CIs were away from zero (i.e. the OTUs had detectable growth).

Bacterial growth responses to climate change and the interaction types between warming and altered precipitation.

The growth rates ( A ), and responses of soil bacteria to warming and altered precipitation ( B ) at the whole community level. The growth rates ( C ), and responses of the dominant bacterial phyla ( D ) had similar trends with that of the whole community. Error bars depict means ± SD (n = 3). Different letters indicate significant differences between climate treatments (p < 0.05). The p-values were calculated using a two-tailed Student’s t -test. Two-way ANOVA was used to examine the effects of climate factors on bacterial growth (**: p ≤ 0.01, ***: p ≤ 0.001, ns: no significance). ‘W×P’: the interaction effects of warming and altered precipitation; ‘W×D’: warming and drought scenario; ‘W×W’: warming and wet scenario.

Growth of the major bacterial phyla was also negatively influenced by the individual climate factors ( Figure 3C and D ). The antagonistic interactions of T and P were prevalent among the major phyla (except Bacteroidetes showed the additive interaction between drought and warming). We also found the significant smaller combined effect sizes of warming × wet in the major phyla compared with that of warming × drought (p < 0.05), such as Actinobacteria, Bacteroidetes and Betaproteobacteria, indicating higher degree of antagonism. In Actinobacteria and Bacteroidetes, the effect of wet and warming neutralized each other, as the combined effect of these two factors had no effect on growth.

Phylogeny for the species whose growth subjected to different factor interactions

We constructed a phylogenetic tree including all 18 O incorporators in all six climate treatments ( Figure 4A ). The single-factor effects on the growth of incorporators tended to be negative ( Figure 4B ): Warming (T + nP) reduced the growth of 75% of the taxonomic groups, which was followed by drought and wet (74% and 67%, respectively). Warming × drought and warming × wet had the smaller impacts on the growth of incorporators, compared with the single effects (especially T + +P, only 43% of incorporators showed negative growth responses). The interaction type of T and P on the growth of ~70% incorporators was antagonistic (i.e. the combined effect size is smaller than the additive expectation) ( Figure 4C ). The weak antagonistic interaction on bacterial growth was dominant under the warming × drought conditions (41% of incorporators), while more incorporators (34%) whose growth subjected to neutralizing effect was found under the warming × wet conditions. These findings were robust at a subOTUs level by the zero-radius OTU (ZOTU) analysis ( Figure 3—figure supplement 1 and Figure 4—figure supplement 1 ).

The growth responses and phylogenetic relationship of incorporators subjected to different interaction types under two climate scenarios.

A phylogenetic tree of all incorporators observed in the grassland soils ( A ). The inner heatmap represents the single and combined factor effects of climate factors on species growth, by comparing with the growth rates in T 0 nP. The outer heatmap represents the interaction types between warming and altered precipitation under two climate change scenarios. The proportions of positive or negative responses in species growth to single and combined manipulation of climate factors by summarizing the data from the inner heatmap ( B ). The proportions of species growth influenced by different interaction types under two climate change scenarios by summarizing the data from the outer heatmap ( C ).

Figure 4—source data 1

The nearest taxon index (NTI) for incorporators subjected to different interaction types under two climate change scenarios.

Phylogenetic relatedness can provide information on the ecological and evolutionary processes that influenced the emergence of the eco-physiological responses in taxonomic groups ( Evans and Wallenstein, 2014 ). Nearest taxon index (NTI) was used to determine whether the species in a particular growth response are more phylogenetically related to one another than to other species (i.e. close or clustering on phylogenetic tree; Figure 4—source data 1 ). NTI is an indicator of the extent of terminal clustering, or clustering near the tips of the tree ( Evans and Wallenstein, 2014 ; Webb et al., 2002 ). Overall, the most incorporators whose growth was influenced by the antagonistic interaction of T and P showed significant phylogenetic clustering (i.e. species clustered at the phylogenetic branches, indicating close genetic relationship; NTI > 0, p < 0.05). The incorporators whose growth subjected to the additive interaction of warming × drought also showed significant phylogenetic clustering (p < 0.05), but randomly distributed under warming × wet scenario (p = 0.116). In addition, incorporators whose growth is influenced by the synergistic interaction of T and P showed random phylogenetical distribution under both climate scenarios (p > 0.05).

Higher degree of antagonism in warming and wet scenario

We further assigned the antagonistic intensity to the five interaction types on a 5-point scale, from –1 to 3 for synergistic, additive, weak antagonistic, strong antagonistic and neutralizing effect, respectively ( Figure 4—figure supplement 2 ), where the larger values represent higher degree of antagonism. Then, the overall antagonistic intensities of all incorporators under warming × drought and warming × wet scenarios were estimated by weighting the relative proportions of incorporators subjected to different interaction types ( Figure 4—figure supplement 2 ). We found higher overall antagonistic intensity of warming × wet than that of warming × drought, contributing by a higher proportion of incorporators whose growth subjected to neutralizing effect ( Figure 4C and Figure 4—figure supplement 2 ).

Of the total 1373 incorporators, 1218 were shared in both warming × drought and warming × wet scenarios ( Figure 5A ). That is, the difference in interactive effects between warming × drought and warming × wet we observed was due to a within-species change in growth response (i.e. phenotypic plasticity of organisms), rather than changes in species composition (i.e. species sorting). Of these species identified in both warming × drought and warming × wet scenarios, 453 incorporators were assigned a higher degree of antagonistic interaction of warming × wet than that of warming × drought. Further, the growth of 215 incorporators were influenced by the weak antagonistic interaction of warming × drought, and neutralizing effect of warming × wet. The growth response of these 215 species could contribute mainly to the overall growth patterns observed in grassland bacterial community under warming and altered precipitation scenarios, because of the prevalence of weak antagonistic interaction of warming × drought and neutralizing effect of warming × wet ( Figure 4C ).

Within-species shift in interaction types contributed to the variance of the whole community growth response under two climate scenarios.

Venn plots showing the overlaps of incorporators, and their interaction types between two climate scenarios ( A ). The phylogenetic relationship of the 215 incorporators whose growth dynamics were influenced by the weak antagonistic interaction of warming × drought and by the neutralizing effect of warming × wet ( B ). The blue-green bars represent the average growth rates of incorporators across different climate treatments. The heatmap displayed the potential functions associated with carbon and nutrient cycles predicted by Picrust2. The values of function potential were standardized (range: 0–1). ‘W×D’ represents warming × drought and ‘W×W’ represents warming × wet.

Figure 5—source data 1

Species and genomic information of the dominant active taxa in grassland soil under climate change conditions.

We further assessed the potential functional traits of these 215 incorporators subjected to the dominant interaction types by PICRUST2 software ( Figure 5B ). The top three OTUs with the highest growth rates possessed extremely high species abundance ( Figure 5—source data 1 ). The three taxa also possessed a higher functional potential related to carbon (C), nitrogen (N), sulfur (S), and phosphorus (P) cycling: the member affiliated to Solirubrobacter (OTU 14), has the high functional potential for aerobic C fixation and CO oxidation, nitrogen assimilation and assimilatory nitrite to ammonia, and phosphatase synthesis and phosphate transport transport-related functions. The members affiliated to the genus Pseudonocardia (OTU 5 and OTU 31), harbor a higher functional potential for aerobic C fixation, aerobic respiration, and CO oxidation, dissimilatory nitrate to nitrite and nitrogen assimilation, and sulfur mineralization functions. Furthermore, we annotated the genomic characteristics by aligning species sequences to the GTDB database (Genome Taxonomy Database), and we found that OTU 14 ( Solirubrobacter ) was predicted to have larger genomes and proteomes ( Figure 5—source data 1 ). All these results suggested that these three species could play essential roles at the species and functional levels of ecosystems.

Microbial populations might respond differently to environmental changes, resulting in differential contributions to ensuing biogeochemical fluxes ( Blazewicz et al., 2020 ). Here, we estimated microbial growth responses by using the qSIP technique to decadal-long warming and altered precipitation regimes in the alpine grassland ecosystem on the Tibetan Plateau, which is considered highly susceptible and vulnerable to climate change ( Ma et al., 2017 ). After a decade of temperature and precipitation regime shift, the pervasive negative impacts of climate factors on soil bacterial growth in alpine grassland ecosystem were observed ( Figure 3 ), which supports our first hypothesis that long-term warming and altered rainfall events consistently reduce microbial growth. Consistent with our findings, a recent experimental study demonstrated that 15 years of warming reduced the growth rate of soil bacteria in a montane meadow in northern Arizona ( Purcell et al., 2022 ). These negative effects of climate factors on microbial growth are likely due to the variation related to availability of soil moisture and organic carbon ( Dieleman et al., 2012 ; Wu et al., 2011 ). Previous evidences suggest that warming has a negative impact on soil carbon pools ( Jansson and Hofmockel, 2020 ; Purcell et al., 2022 ), mainly because of the rapid soil carbon mineralization and respiration ( Melillo et al., 2017 ). Carbon is the critical element in cell synthesis, the reduction of microbially accessible carbon pools may explain the diminished microbial growth after long-term warming. In addition, long-term warming can induce soil water deficiency ( Dieleman et al., 2012 ; Jansson and Hofmockel, 2020 ), thereby slowing microbial growth.

Altered rainfall patterns, resulting in increased aridity or wetter conditions, mediate ecosystem cycling by affecting above- and below-ground biological processes ( Song et al., 2019 ). As soil water availability is reduced, changes in osmotic pressure cause microbial death or dormancy, while others can accumulate solutes to survive under lower water potentials ( Schimel, 2018 ). However, such accumulation of osmolytes could depend on highly energetic expenses ( Boot et al., 2013 ; Jansson and Hofmockel, 2020 ; Schimel et al., 2007 ), resulting in less energetic allocation to growth (trade-offs between microbial growth and physiological maintenance). On the other hand, intensified rainfall patterns alter the composition and life strategies of soil bacteria, enriching the taxa with higher tolerance to frequent drying-rewetting cycles ( Evans and Wallenstein, 2014 ). Such taxa may possess physiological acclimatization, such as synthesizing chaperones to stabilize proteins and thicker cell wall to withstand osmotic pressure ( Schimel et al., 2007 ). These adaptation and acclimation strategies also create physiological costs ( Schimel et al., 2007 ), increasing carbon allocation to physiological maintenance instead of new biomass ( Lipson, 2015 ).

Climate-induced changes in the growth and structure of plant communities can also influence soil microbial growth by altering the amount and quality of plant-derived carbon ( Bardgett et al., 2013 ). Increasing drought reduced the transfer of recently fixed plant carbon to soil bacteria and shifts the bacterial community towards slow growth and drought adaptation ( Fuchslueger et al., 2014 ). A 17-year study of California grassland provided evidence that terrestrial net primary production (NPP) to precipitation gradient are hump-shaped, peaking when precipitation is near the multi-year mean growing season level ( Zhu et al., 2016 ). Reduced NPP under increasing rainfall conditions could affect plant carbon inputs to the soil, ultimately having a negative effect on microbial growth.

Characterizing the interactive effects of multiple global change drivers on microbial physiological traits is important for predicting ecosystem responses and soil biogeochemical processes ( Song et al., 2019 ; Zhu et al., 2016 ). In this study, a decade-long experiment revealed that bacterial growth in alpine meadows is primarily influenced by the antagonistic interaction between T and P ( Figures 3 and 4 ). Similarly, a range of ecosystem processes have been revealed to be potentially subject to antagonistic interactions between climate factors, for instance, net primary productivity ( Shaw et al., 2002 ), soil C storage and nutrient cycling processes ( Dieleman et al., 2012 ; Wu et al., 2011 ; Larsen et al., 2011 ). Reduced precipitation can mute organic carbon mineralization by inhibiting soil respiration, which could maintain a relatively adequate soil carbon content and explain the diminished negative effects on microbial growth by the combined manipulation of warming and drought ( Jansson and Hofmockel, 2020 ; Wu et al., 2011 ). Conversely, enhanced precipitation could stimulate SOM decomposition, causing further loss of soil carbon under warming conditions ( Zhou et al., 2022 ). However, increased rainfall can also alleviate the moisture limitation on plant growth induced by warming, increasing plant-derived carbon inputs ( Jansson and Hofmockel, 2020 ; Wu et al., 2011 ). The increased carbon inputs may alleviate microbial carbon limitation in soil, which partly explains the higher microbial growth rates under the combined treatment of warming and enhanced precipitation than that in the single climate factor treatments.

The degree of phylogenetic relatedness can indicate the processes that influenced community assembly, like the extent a community is shaped by environmental filtering (clustered by phylogeny) or competitive interactions (life strategy is phylogenetically random distribution) ( Evans and Wallenstein, 2014 ; Webb et al., 2002 ). The results showed that the incorporators whose growth was influenced by the antagonistic interaction of T and P showed significant phylogenetic relatedness, indicating the occurrence of taxa more likely shaped by environment filtering (i.e. selection pressure caused by changes in temperature and moisture conditions). In contrast, the growing taxa affected by synergistic interactions of T and P showed random phylogenetic distributions ( Figure 4—source data 1 ), which may be explained by competition between taxa with similar eco-physiological traits or changes in genotypes (possibly through horizontal gene transfer) ( Evans and Wallenstein, 2014 ). We also found that the extent of phylogenetic relatedness to which taxa groups of T and P interaction types varied by climate scenarios, suggesting that different climate history processes influenced the ways bacteria survive temperature and moisture stress.

About one-third of bacterial species had growth with higher levels of antagonistic interaction of warming × wet than that of warming × drought ( Figure 5A ). By annotating the genomic information, we further found that the species with the high growth rate (OTU 14, Solirubrobacter ) has a relatively larger genome size and protein coding density ( Figure 5—source data 1 ), indicating larger gene and function repertoires. A previous study showed that the genus Solirubrobacter detected in the Thar desert of India is involved in multiple biochemical processes, such as N and S cycling ( Sivakala et al., 2018 ). Members in the genus Solirubrobacter are also considered to contribute positively to plant growth ( Liu et al., 2020 ), and can be used to predict the degradation level of grasslands, indicating the critical roles on maintaining ecosystem services ( Yan et al., 2022 ). This is, however, still to be verified, as the functional output from PICRUSt2 is less likely to resolve rare environment-specific functions ( Dieleman et al., 2012 ). This suggests the development of methods combining qSIP with metagenomes and metatranscriptomes to assess the functional shifts of active microorganisms under global change scenarios. Note that the experimental parameters such as DNA extraction and PCR amplification efficiencies also have significant effects on the accuracy of growth assessment. This alerts the need to standardize experimental practices to ensure more realistic and reliable results.

The evaluation of ecosystem models based on results obtained from single-factor experiments usually overestimate or underestimate the impact of global change on ecosystems, because the interactions between climate factors may not be simply additive ( Dieleman et al., 2012 ; Wu et al., 2011 ; Zhou et al., 2022 ). Our results demonstrated that both warming and altered precipitation negatively affect the growth of grassland bacteria; However, the combined effects of warming and altered precipitation on the growth of ~70% soil bacterial taxa were smaller than the single-factor effects, suggesting antagonistic interaction. This suggests the development of multifactor manipulation experiments in precise prediction of future ecosystem services and feedbacks under climate change scenarios.

Study design and soil sampling

The warming-by-precipitation experiment was established in 2011 at the Haibei National Field Research Station of Alpine Grassland Ecosystem (37°37′N, 101°33′E, with elevation 3215 m), which is located on the northeastern Tibetan Plateau in Qinghai Province, China. The climate type is a continental monsoon with mean annual precipitation of 485 mm and the annual average temperature approximately –1.7℃. The high rainfall and temperature mainly occur in the peak-growing season (from July to August Liu et al., 2018 ). The soils are Mat-Gryic Cambisols, with the average pH value of surface soil (0–10 cm) being 6.4 ( Ma et al., 2017 ).

The experimental design has been described previously in Ma et al., 2017 . Briefly, experimental plots were established in an area of 50 m × 110 m in 2011, using a randomized block design with warming and altered precipitation treatments. Each block contained six plots (each plot was 1.8 m × 2.2 m), crossing two levels of temperature [ambient temperature (T 0 ), elevated temperature of top 5 cm layer of the soil by 2℃ (T + )], and three levels of precipitation [natural precipitation (nP, represents ambient condition), 50% reduced precipitation (-P, represents ‘drought’ condition) and 50% enhanced precipitation (+P, represents ‘wet’ condition)]. In the warming treatments, two infrared heaters (1000 mm length, 22 mm width) were suspended in parallel at 150 cm above the ground within each plot. Rain shelters were used to control the received precipitation in the experimental plots. Four ‘V’-shaped transparent polycarbonate resin channels (Teijin Chemical, Japan) were fixed at a 15° angle, above the infrared heaters, to intercept 50% of incoming precipitation throughout the year. The collected rainfall from the drought plots was supplied to the wet plots manually after each precipitation event by sprinklers, increasing precipitation by 50%. To control for the effects of shading caused by infrared heaters, two ‘dummy’ infrared heaters and four ‘dummy’ transparent polycarbonate resin channels were installed in the control plots. Each treatment had six replicates, resulting in thirty-six plots.

Soil samples for qSIP incubation were collected in August 2020. Considering the cost of qSIP experiment (including the use of isotopes and the sequencing of a large number of DNA samples), we randomly selected three out of the six plots, serving as three replicates for each treatment. In each plot, three soil cores of the topsoil (0–5 cm in depth) were randomly collected and combined as a composite sample, which can be considered as a mixture of rhizosphere and bulk soils. Each sampling point was as far away from infrared heaters as possible to minimize the impact of physical shading on the plants. The fresh soil samples were shipped to the laboratory and sieved (2 mm) to remove root fragments and stones.

18 O-qSIP incubation

The incubations were similar to those reported in a previous study ( Ruan et al., 2023 ). Soil samples of ambient temperature treatments (including T 0 -P, T 0 nP, and T 0 +P) were air-dried at 14℃ (average temperature across the growth season), while the soil samples of warming treatments (including T + -P, T + nP, and T + +P) were air-dried at 16℃ (increased temperature of 2℃). There is no violent air convection in the incubator and the incubation temperature is relatively low (compared to 25℃ used in previous studies), resulting slower evaporation and no significant discoloration caused by severe soil dehydration after 48 hr. A portion of the air-dried soil samples was taken as the pre-wet treatment (i.e., before incubation without H 2 O addition). We incubated the air-dried soils (2.00 g) with 400 μl of 98 atom% H 2 18 O ( 18 O treatment) or natural abundance water ( 16 O treatment) in the dark for 2 d by using sterile glass aerobic culture bottles (Diameter: 29 mm; Height: 54 mm). After incubation, soils were destructively sampled and stored at –80℃ immediately. A total of 54 soil samples, including 18 pre-wet samples (6 treatments × 3 replicates) and 36 incubation samples (6 treatments × 3 replicates × 2 types of H 2 O addition), were collected.

DNA extraction and isopycnic centrifugation

Total DNA from all the collected soil samples was extracted using the FastDNA SPIN Kit for Soil (MP Biomedicals, Cleveland, OH, USA) according to the manufacturer’s instructions. Briefly, the mechanical cell destruction was attained by multi-size beads beating at 6 m s –1 for 40 s, and then FastDNA SPIN Kit for Soil (MP Biomedicals, Cleveland, OH, USA) was used for DNA extraction. All DNA samples were extracted by the same person within 2–3 hr, and a unifying procedure of cell lysis and DNA extraction was used. The concentration of extracted DNA was determined fluorometrically using Qubit DNA HS (High Sensitivity) Assay Kits (Thermo Scientific, Waltham, MA, USA) on a Qubit 4 fluorometer (Thermo Scientific, Waltham, MA, USA). The DNA samples of 2-d incubation were used for isopycnic centrifugation, according to a previous publication ( Ruan et al., 2023 ). Briefly, 3 μg DNA were added into 1.85  g ml –1 CsCl gradient buffer (0.1 M Tris-HCl, 0.1 M KCl, 1 mM EDTA, pH = 8.0) with a final buoyant density of 1.718  g ml –1 . Approximately 5.1 ml of the solution was transferred to an ultracentrifuge tube (Beckman Coulter QuickSeal, 13 mm × 51 mm) and heat-sealed. All tubes were spun in an Optima XPN-100 ultracentrifuge (Beckman Coulter) using a VTi 65.2 rotor at 177000  g at 18℃ for 72  h with minimum acceleration and braking.

Immediately after centrifugation, the contents of each ultracentrifuge tube were separated into 20 fractions (~250  μl each fraction) by displacing the gradient medium with sterile water at the top of the tube using a syringe pump (Longer Pump, LSP01‐2 A, China). The buoyant density of each fraction was measured using a digital hand-held refractometer (Reichert, Inc, Buffalo, NY, USA) from 10 μl volumes. Fractionated DNA was precipitated from CsCl by adding 500  μl 30% polyethylene glycol (PEG) 6000 and 1.6 M NaCl solution, incubated at 37℃ for 1 hr and then washed twice with 70% ethanol. The DNA of each fraction was then dissolved in 30  μl of Tris‐EDTA buffer.

Quantitative PCR and sequencing

Total 16S rRNA gene copies for DNA samples of all the fractions were quantified using the primers for V4-V5 regions: 515F (5′‐GTG CCA GCM GCC GCG G‐3′) and 907R (5′‐CCG TCA ATT CMT TTR AGT TT‐3′) ( Guo et al., 2018 ). The V4-V5 primer pairs were chosen to facilitate integration and comparison with data from previous studies ( Ruan et al., 2023 ; Zhang et al., 2016 ). Plasmid standards were prepared by inserting a copy of purified PCR product from soil DNA into Escherichia coli . The E. coli was then cultured, followed by plasmid extraction and purification. The concentration of plasmid was measured using Qubit DNA HS Assay Kits. Standard curves were generated using 10‐fold serial dilutions of the plasmid. Each reaction was performed in a 25 μl volume containing 12.5 μl SYBR Premix Ex Taq (TaKaRa Biotechnology, Otsu, Shiga, Japan), 0.5 μl of forward and reverse primers (10 μM), 0.5 μl of ROX Reference Dye II (50 ×), 1 μl of template DNA and 10 μl of sterile water. A two-step thermocycling procedure was performed, which consisted of 30 s at 95℃, followed by 40 cycles of 5 s at 95℃, 34 s at 60℃ (at which time the fluorescence signal was collected). Following qPCR cycling, melting curves were conducted from 55 to 95℃ with an increase of 0.5℃ every 5 s to ensure that results were representative of the target gene. Average PCR efficiency was 97% and the average slope was –3.38, with all standard curves having R 2 ≥ 0.99.

The DNA of pre-wet soil samples (unfractionated) and the fractionated DNA of the fractions with buoyant density between 1.703 and 1.727 g ml –1 (7 fractions) were selected for 16S rRNA gene sequencing by using the same primers of qPCR (515F/907R). The fractions with density between 1.703 and 1.727 g ml –1 were selected because they contained more than 99% gene copy numbers of each sample. A total of 270 DNA samples [18 total DNA samples of prewet soil +252 fractionated DNA samples (6 treatments × 3 replicates × 2 types of water addition × 7 fractions)] were sequenced using the NovaSeq6000 platform (Genesky Biotechnologies, Shanghai, China).

The raw sequences were quality-filtered using the USEARCH v.11.0 ( Edgar, 2010 ). In brief, the paired-end sequences were merged and quality filtered with ‘fastq_mergepairs’ and ‘fastq_filter’ commands, respectively. Sequences < 370 bp and total expected errors > 0.5 were removed. Next, ‘fastx_uniques’ command was implemented to identify the unique sequences. Subsequently, high-quality sequences were clustered into operational taxonomic units (OTUs) with ‘cluster_otus’ commandat a 97% identity threshold, and the most abundant sequence from each OTU was selected as a representative sequence. The taxonomic affiliation of the representative sequence was determined using the RDP classifier (version 16) ( Wang et al., 2007 ). In total, 19,184,889 reads of the bacterial 16S rRNA gene and 6,938 OTUs were obtained. The sequences were uploaded to the National Genomics Data Center (NGDC) Genome Sequence Archive (GSA) with accession numbers CRA007230.

Quantitative stable isotope probing calculations

As 18 O labeling occurs during cell growth via DNA replication, the amount of 18 O incorporated into DNA was used to estimate the growth rates of active taxa. The density shifts of OTUs between 16 O and 18 O treatments were calculated following the qSIP procedures ( Hungate et al., 2015 ; Koch et al., 2018 ). Briefly, the number of 16S rRNA gene copies per taxon (e.g. genus or OTU) in each density fraction was calculated by multiplying the relative abundance (acquisition by sequencing) by the total number of 16S rRNA gene copies (acquisition by qPCR). Then, the GC content and molecular weight of a particular taxon were calculated. Further, the change in 18 O isotopic composition of 16S rRNA genes for each taxon was estimated. We assumed an exponential growth model over the course of the incubations. The growth rate is a function of the rate of appearance of 18 O-labeled 16S rRNA genes. Therefore, the growth rate of taxon i was calculated as:

where N TOTAL it is the number of total gene copies for taxon i and N LIGHT it represents the unlabeled 16S rRNA gene abundances of taxon i at the end of the incubation period (time t ). N LIGHT it is calculated by a function with four variables: N TOTAL it , average molecular weights of DNA (taxon i ) in the 16 O treatment ( M LIGHT i ) and in the 18 O treatment ( M LAB i ), and the maximum molecular weight of DNA that could result from assimilation of H 2 18 O ( M HEAVY i ) ( Koch et al., 2018 ). We further calculated the average growth rates (represented by the production of new16S rRNA gene copies of each taxon per g dry soil per day) along the incubation, using the following equation ( Stone et al., 2021 ):

where t is the incubation time (day). All data calculations were performed using the qSIP pipeline Source code 1 in R (Version 3.6.2) ( Streit et al., 2014 ).

Single and combined effects of climate change factors

To address the effects of warming and altered precipitation on microbial growth rates, three single-factor effects (warming, 50% reduced precipitation only, and 50% enhanced precipitation only) and two combined effects (combined warming and reduced precipitation manipulation and combined warming and enhanced precipitation manipulation) were calculated by the natural logarithm of response ratio (lnRR), representing the response of microbial growth rates in the climate change treatment compared with that in the ambient treatment ( Yue et al., 2017 ). The lnRR for growth rates was calculated as:

where X t is the observed growth rates in climate treatment and X c is that in control. 95% confidence interval (CI) was estimated using a bootstrapping procedure with 1000 iterations ( Ruan et al., 2023 ). If the 95% CI did not overlap with zero, the effect of treatment on microbial growth is significant.

The interaction between warming and altered precipitation

All six climate treatments were divided into two groups, warming combined with reduced precipitation scenario (Warming × Drought), and warming combined with enhanced precipitation scenario (Warming × Wet), by using the ambient temperature and precipitation treatment (T 0 nP) as control ( Figure 1A ). Hedges’ d , an estimate of the standardized mean difference, was used to assess the interaction effects of warming × drought (i.e. reduced precipitation) and warming × wet (i.e. enhanced precipitation), respectively ( Yue et al., 2017 ). The interaction effect size ( d I ) of warming × drought or warming × wet was calculated as:

where X c , X A , X B , and X AB are growth rates in the control, treatment groups of factor A, B, and their combination (AB), respectively. 95% CI was estimated using a bootstrapping procedure with 1000 iterations. The s and J ( m ) are the pooled standard deviation and correction term for small sample bias, respectively, which were calculated by the following equations:

where n c , n A , n B , and n AB are the sample sizes, and s c , s A , s B , and s AB are the standard deviations in the control, experimental groups of A, B, and their combination (AB), respectively.

The interaction types between warming and altered precipitation were mainly classified into three types, that is additive, synergistic and antagonistic, according to the single-factor effects and 95% CI of d I . If the 95% CI of d I overlapped with zero, the interactive effect of warming and altered precipitation was additive. The synergistic interaction included two cases: (1) the upper limit of 95% CI of d I < 0 and the single-factor effects were either both negative or have opposite directions; (2) the lower limit of 95% CI of d I > 0 and both single-factor effects were positive. The antagonistic interaction also included two cases: (1) the upper limit of 95% CI of d I < 0 and both single-factor effects were positive; (2) the lower limit of 95% CI of d I > 0 and the single-factor effects were either both negative or have opposite directions ( Yue et al., 2017 ). We further divided antagonistic interaction into three sub-categories: weak antagonistic interaction, strong antagonistic interaction, and neutralizing effect, by comparing the single-factor and combined effect sizes ( Figure 1B ). The weak antagonistic interaction determined if the combined effect size was larger than the single-factor effect sizes, but smaller than their expected additive effect. The strong antagonistic interaction determined if the combined effect size was smaller than the single-factor effect sizes but not equal to zero. The neutralizing effect represented the combined effect size is equal to zero, and at least one single-factor effect size is not equal to zero.

Statistical analyses

Uncertainty of growth rates (95% CI) was estimated using a bootstrapping procedure with 1000 iterations ( Ruan et al., 2023 ). The cumulative growth rates at the phylum-level were estimated as the sum of taxon-specific growth rates of those OTUs affiliated to the same phylum. Significant differences of bacterial growth rates for each group between climate treatments were assessed by two-way ANOVA in R (version 3.6.2). Phylogenetic trees were constructed in Galaxy /DengLab ( http://mem.rcees.ac.cn:8080 ) with PyNAST Alignment and FastTree functions ( Caporaso et al., 2010 ; Price et al., 2009 ). The trees were visualized and edited using iTOL ( Letunic and Bork, 2016 ). To estimate the phylogenetic patterns of incorporators whose growth subjected to different factor interaction types, the nearest taxon index (NTI) was calculated by the ‘picante’ package in R (version 3.6.2; Webb et al., 2002 ). NTI with values larger than 0 and their p values less than 0.05 represent phylogenetic clustering. The p values of NTI between 0.05 and 0.95 represent random phylogenetic distributions. KO gene annotation of taxa was performed by PICRUSt2 (Phylogenetic Investigation of Communities by Reconstruction of Unobserved States), which predicted functional abundances based on marker gene sequences ( Dieleman et al., 2012 ). The marker genes related to carbon (C), nitrogen (N), sulfur (S), and phosphorus (P) cycling were selected according to the conclusions reported in previous documents ( Dai et al., 2020 ; Llorens-Marès et al., 2015 ; Nelson et al., 2015 ).

The sequence data were uploaded to the National Genomics Data Center (NGDC) Genome Sequence Archive (GSA) with accession number CRA007230.

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Author details

• State Key Laboratory of Herbage Improvement and Grassland Agro-Ecosystems, College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou, China
• Jiangsu Provincial Key Lab for Solid Organic Waste Utilization, Jiangsu Collaborative Innovation Center for Solid Organic Waste Resource Utilization, Nanjing Agricultural University, Nanjing, China

Contribution

Competing interests, for correspondence.

• Institute of Ecology, College of Urban and Environmental Sciences, and Key Laboratory for Earth Surface Processes of the Ministry of Education, Peking University, Beijing, China

National Science Foundation of China (42277100)

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Acknowledgements

This work was supported by the National Science Foundation of China [42277100 (NL)].

Version history

• Sent for peer review: May 23, 2023
• Preprint posted: June 8, 2023 (view preprint)
• Preprint posted: October 11, 2023 (view preprint)
• Preprint posted: April 3, 2024 (view preprint)
• Version of Record published: April 22, 2024 (version 1)

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Open Access

Peer-reviewed

Research Article

Effects of Precipitation Increase on Soil Respiration: A Three-Year Field Experiment in Subtropical Forests in China

Affiliations South China Botanical Garden, Chinese Academy of Sciences, Guangzhou, China, Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan, China

* E-mail: [email protected]

Affiliation Department of Biological Sciences, Tennessee State University, Nashville, Tennessee, United States of America

Affiliation South China Botanical Garden, Chinese Academy of Sciences, Guangzhou, China

• Qi Deng, 
• Dafeng Hui, 
• Deqiang Zhang, 
• Guoyi Zhou, 
• Juxiu Liu, 
• Shizhong Liu, 
• Guowei Chu, 

• Published: July 23, 2012
• https://doi.org/10.1371/journal.pone.0041493
• Reader Comments

15 Oct 2012: Deng Q, Hui D, Zhang D, Zhou G, Liu J, et al. (2012) Correction: Effects of Precipitation Increase on Soil Respiration: A Three-Year Field Experiment in Subtropical Forests in China. PLOS ONE 7(10): 10.1371/annotation/1f49fc5e-e3f9-4b90-b555-97a54990ac3f. https://doi.org/10.1371/annotation/1f49fc5e-e3f9-4b90-b555-97a54990ac3f View correction

The aim of this study was to determine response patterns and mechanisms of soil respiration to precipitation increases in subtropical regions.

Methodology/Principal Findings

Field plots in three typical forests [i.e. pine forest (PF), broadleaf forest (BF), and pine and broadleaf mixed forest (MF)] in subtropical China were exposed under either Double Precipitation (DP) treatment or Ambient Precipitation (AP). Soil respiration, soil temperature, soil moisture, soil microbial biomass and fine root biomass were measured over three years. We tested whether precipitation treatments influenced the relationship of soil respiration rate ( R ) with soil temperature ( T ) and soil moisture ( M ) using R =  ( a+cM )exp( bT ), where a is a parameter related to basal soil respiration; b and c are parameters related to the soil temperature and moisture sensitivities of soil respiration, respectively. We found that the DP treatment only slightly increased mean annual soil respiration in the PF (15.4%) and did not significantly change soil respiration in the MF and the BF. In the BF, the increase in soil respiration was related to the enhancements of both soil fine root biomass and microbial biomass. The DP treatment did not change model parameters, but increased soil moisture, resulting in a slight increase in soil respiration. In the MF and the BF, the DP treatment decreased soil temperature sensitivity b but increased basal soil respiration a , resulting in no significant change in soil respiration.

Conclusion/Significance

Our results indicate that precipitation increasing in subtropical regions in China may have limited effects on soil respiration.

Citation: Deng Q, Hui D, Zhang D, Zhou G, Liu J, Liu S, et al. (2012) Effects of Precipitation Increase on Soil Respiration: A Three-Year Field Experiment in Subtropical Forests in China. PLoS ONE 7(7): e41493. https://doi.org/10.1371/journal.pone.0041493

Editor: Ben Bond-Lamberty, DOE Pacific Northwest National Laboratory, United States of America

Received: May 6, 2012; Accepted: June 27, 2012; Published: July 23, 2012

Copyright: © 2012 Deng et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Funding: This work was financially supported by National Basic Research Program of China (2009CB42110×), Strategic Priority Research Program-Climate Change: Carbon Budget and Relevant Issues of Chinese Academy of Sciences (XDA05050205), Dinghushan Forest Ecosystem Research Station, and the National Science Foundation (0933958). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Soil respiration in terrestrial ecosystems plays an important role in global carbon cycling and climate change [1] – [4] . However, our understanding of precipitation impacts on soil respiration is still very limited, particularly in tropical and subtropical forests [5] . As greater intensity of precipitation and more severe droughts and floods are predicted in the future [6] – [7] , such changes in precipitation may have significant influences on soil moisture and soil respiration in terrestrial ecosystems. Compared to drought, few studies have been done on the influence of heavy precipitation on soil respiration [8] – [14] . Considering that tropical and subtropical forests contain more than 25% of the carbon in the terrestrial biosphere, it is imperative to improve our mechanistic understanding of soil respiration responses to precipitation and soil moisture changes [15] , [16] .

Soil respiration includes both respiration of living roots and microbial respiration resulted from microbial decomposition of litter and soil organic matter [3] , [5] , [10] , [12] . Root activity and microbial decomposition are often subject to both environmental factors and substrate changes related to phenological processes [17] – [20] . Any changes in root biomass, soil organic matter, root and microbial activities due to precipitation change could influence soil respiration. Like many biological processes, soil respiration is also influenced by soil temperature and moisture in many different ecosystems [3] , [21] – [23] . While it is generally accepted that global warming could influence the relationship of soil respiration and temperature, how precipitation treatments would influence soil respiration and its relationship to soil moisture has not been well investigated. When treatments such as warming, precipitation, or CO 2 concentration changes are applied, response variables may respond directly to changes in environmental factors as well as alter their relationships with environmental factors. Thus, soil respiration responses to precipitation treatments could be caused by either changes in environmental factors such as soil temperature and moisture, or functional changes – which are defined as changes in model parameters of soil respiration with soil temperature and moisture, or both [23] . For example, functional change due to a change in soil temperature sensitivity may increase or decrease soil respiration even when soil temperature is not influenced by precipitation treatments. Functional change could be attributed to the changes in phenological process, substrate or microbial activity in an ecosystem [2] , [5] , [16] , [23] .

Changes in soil moisture under different precipitation treatments could influence the responses of soil respiration to precipitation. There is no doubt that precipitation is usually the driving factor of the dynamics in soil moisture. However, soil water storage after precipitation events depends on vegetation types and covers, soil characteristics (e.g., infiltration rates, slopes, textures, depths, impermeable layers), and losses to deep drainage, lateral flow, and evaporation [24] . Thus, the response of soil moisture to precipitation treatments often varies in different ecosystems. For example, drought treatments using automated retractable curtains reduced soil moisture by 32–48%, 15–61%, and 19–25% at three heathlands [25] , and double precipitation increased soil moisture by only 10% in Oklahoma grassland [10] . How precipitation changes influence soil moisture in subtropical forests may have significant impacts on soil respiration.

Functional changes (i.e. changes in model parameters of soil respiration with soil temperature and moisture) reflect underlying biological changes in the response of soil respiration to precipitation changes. Many empirical models of soil respiration and soil moisture have been developed [26] – [29] . Response of soil respiration to soil moisture is usually nonlinear, with soil respiration increases with soil moisture increases, levels off at high soil moisture, and even decreases when soil moisture is too high [3] , [28] , [29] . However, linear regression seems to work well in many different ecosystems, including boreal forests, sub-Antarctic island ecosystems, temperate grasslands, temperate forests, Mediterranean ecosystems, and particularly, tropical and subtropical forests [22] , [30] – [34] . The slope of the linear regression model can be considered as soil moisture sensitivity, as it reflects an average change in soil respiration due to one unit change of soil moisture. While many precipitation manipulation experiments have been performed [9] – [15] , [21] , [22] , only a few studies have attempted to study the soil moisture sensitivity change under climate change, particularly precipitation [3] , [35] – [37] .

Another important functional relationship is the response of soil respiration to soil temperature [2] , [38] , [39] . Soil temperature is the major control of soil respiration due to its influences on the kinetics of microbial decomposition, root respiration and diffusion of enzymes and substrates [32] , [40] . Numerous studies have focused on the responses of soil respiration to soil temperature. The most widely used model is an exponential equation ( R = R 0 exp( bT )) where R is soil respiration, T is soil temperature, and parameter R 0 is basal soil respiration, and b is related to soil temperature sensitivity ( Q 10  = exp(10 b ) [41] , [42] . Many studies reported that soil temperature sensitivity may decrease under high temperature treatments [2] , [30] , [38] , [39] and increase under low temperature [38] , [40] – [43] . Several studies also indicated that soil water stress or excess may decrease soil temperature sensitivity of soil respiration [27] , [42] , [44] . Since soil temperature and soil moisture may interactively regulate soil respiration in field conditions, relationships of soil respiration with both soil temperature and moisture have also been proposed [20] , [36] , [45] . Whether and how soil moisture and temperature sensitivities vary with precipitation increase have not been well investigated [5] , [14] .

We conducted a precipitation manipulation field experiment in subtropical forests in Southern China with an overall aim to understand the responses of soil respiration to precipitation increase. We selected three common forests at the study site, established two precipitation treatments in each forest, and measured soil respiration over three years. Double precipitation was realized through automatic interception-redistribution systems that delivering intercepted precipitation from nearby plots of the same size [10] . Adjacent control plots received ambient precipitation (AP). We addressed the following three questions in this study: 1) what are the response patterns of soil respiration to precipitation increase in the subtropical forests? 2) Do different forest sites respond differently to precipitation increase? 3) Does precipitation increase influence soil temperature and moisture sensitivities? The conclusions obtained in this study will enrich our knowledge of soil respiration responses to precipitation changes in subtropical forests in China and may have potentially significant implications for terrestrial ecosystem carbon cycling.

Materials and Methods

Ethics statement.

The study site is maintained by the South China Botanical Garden, Chinese Academy of Sciences. The location is within the Dinghushan Forest Ecosystem Research Station, Chinese Ecosystem Research Network (CERN). All necessary permits were obtained for the described field study. The field study did not involve endangered or protected species. Data will be made available upon request.

Site Description

The study site is located in the center of Guangdong Province in southern China (112°13′39′′–112°33′41′′ E, 23°09′21′′–23°11′30′′ N). Climate in the region is typical south subtropical monsoon climate, with mean annual temperature of 21.4°C, and mean annual precipitation of 1956 mm [33] , of which nearly 80% falls in the hot-humid wet/rainy season (April-September) and 20% in the dry season (October-March). The bedrock is sandstone and shale. Three common subtropical forests (at elevations ranging from 200 to 300 m, less than 500 m from one another and facing the same slope direction) were selected including a coniferous Masson pine forest (PF), a conifer and broadleaf mixed forest (MF), and an evergreen broadleaf forest (BF). The three forests also represent forests in early-, middle-, and advanced-successional stages in the region [46] , [47] . Soil properties and major stand information are listed in Table 1 . The PF (approximately 22 ha), originally planted by local people in the 1950 s, was dominated by Pinus massoniana in the tree layer and Baeckea frutescens , Rhodomyrtus tomenosa , and Dicranopteris linearis in the shrub and herb layers. The MF (approximately 557 ha) was developed from artificial pine forest with a gradual invasion of some pioneer broadleaf species through natural succession. The upper canopy of the community is dominated by Schima superba , Castanopsis chinensis , and Craibiodendron scleranthum var. kwangtungense . Artificial disturbances have not occurred in the MF for about 100 years. The BF (approximately 218 ha) located in the central area of the reserve was dominated by Castanopsis chinensis , Cryptocarya concinna , Schima superba , Machilus chinensis without any Pinus massoniana . No disturbance was recorded for the past 400 years in the BF [37] – [38] .

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https://doi.org/10.1371/journal.pone.0041493.t001

Experimental Design

We used a two-factor experimental design considering forest ecosystem type and precipitation treatment. At each forest site, a randomized block design was used with three blocks. In each block, one double precipitation (DP) treatment plot and one control plot were arranged. For the DP plot, precipitation was intercepted in a nearby plot with same size as the treatment plot using transparent polyvinyl chloride (PVC) sheer roof and was redistributed to the DP plot using pipes similar to those used in [10] . The control plot that received ambient precipitation (AP) was built next to the treatment plot. Each plot was 3×3 m 2 and the distance between the DP and AP plots was more than one meter.

Soil Respiration Measurements

Five PVC soil collars (80 cm 2 in area and 5 cm in height) were permanently installed 3 cm into the soil in each plot in November 2006. The distance between adjacent collars was more than 50 cm. Soil respiration was measured three times a month from January 2007 to December 2008 and two times a month in 2009 using a Li-6400 infrared gas analyzer (Li-COR, Inc., Lincoln, Nebraska, USA) connected to a Li-6400-09 soil respiration chamber (9.55 cm diameter) (Li-COR, Inc., Lincoln, Nebraska, USA). The measurements were made between 9∶00 am and 12∶00 pm local time. Previous work at this study site has demonstrated that soil respiration in forests measured during this period was close to daily mean [30] , [48] . Soil respiration was measured three cycles for each soil collar and the CO 2 concentration change in the chamber to complete one cycle was set as 10 ppm above the set point. Soil respiration in a treatment plot was calculated as the mean of five collar measurements (the measurement at five collars in a plot mostly differed by less than 5% at any measurement period). Soil temperature at 5 cm below the soil surface was also monitored with a thermocouple sensor attached to the respiration chamber during the soil respiration measurement. Volumetric soil moisture of the top 5 cm soil layer was measured on five random locations within a treatment plot using a PMKit [34] at the same time when the soil respiration measurements were being taken.

Soil Microbial Biomass and Fine Root Biomass Measurements

Soil samples were collected in February 2008 to determine soil microbial biomass C content, and three more times in May 2008, August 2008 and November 2008. Two samples of six cores (2.5 cm diameter) were randomly collected from each plot in the three forests. After removing roots and plant residues, the composited samples were immediately sieved through a 2-mm mesh sieve. The soil microbial biomass carbon was determined by the fumigation-extraction technique. The soil microbial biomass carbon was extracted with potassium sulfate on both fumigated and unfumigated soil [49] , [50] . The carbon content of the extract was tested and the biomass was calculated based on the difference between the carbon content of fumigated vs. the unfumigated soil [49] , [50] .

To measure fine root biomass (diameter≤3 mm), we collected soil cores (0–20 cm depth) in February 2008 using a 10 cm diameter stainless-steel corer, and three more times in April 2008, August 2008 and October 2008. Each sample was randomly collected from each plot in each forest. Fine roots were separated by washing and sieving, dried at 60°C for 48 h and weighed.

Statistical Analysis

Soil respiration rate and soil temperature in a plot were calculated as the means of five collar measurements. Soil moisture was calculated as the mean of five measurements at random locations in a plot. We used repeated measure Analysis of Variance (ANOVA) to test the differences in soil respiration rate, soil temperature and soil moisture among forests, precipitation treatments, and years. Each treatment was replicated three times (three blocks). Multiple comparisons (Least Significant Difference, LSD method) were conducted if significant effects of forest ecosystem types, precipitation treatments or years were found. Previous work at study sites demonstrated that soil respiration increases exponentially with soil temperature and linearly with soil moisture [30] , [33] , [34] . Thus, we first developed the relationship between soil respiration and soil temperature with an exponential function and the relationship between soil respiration and soil moisture with a linear regression mode. Considering that soil temperature and moisture may interactively regulate soil respiration, we also fit soil respiration ( R ) with soil temperature ( T ) and soil moisture ( M ) together using R =  ( a+cM )exp( bT ), where a is parameter related to basal soil respiration when both T  = 0 and M  = 0; b and c are parameters related to the soil temperature and moisture sensitivities of soil respiration, respectively. Like most studies, we used measurements of soil respiration, soil temperature and moisture of whole years here. One caveat of this approach was that seasonal variations of tree roots growth, carbon substrate in the soils, and soil microbial community would influence soil respiration, but were difficult to quantify. Non-linear least square method was used to derive the model parameters using SAS NLIN procedure [51] . Soil temperature and moisture sensitivities were derived for different precipitation treatments in the three forests. All data analyses were carried out using SAS software Version 9.1 [51] (SAS Institute Inc., Cary, NC, USA).

Effects of Precipitation Treatments on Soil Temperature and Moisture

There were strong seasonal variations of precipitation in all three years, with intensive precipitation occurring from April through September (i.e., wet season) ( Figure 1 ). The annual precipitation amount was 1341.6, 2925.8, and 1864.4 mm in 2007, 2008, and 2009, respectively. The very high precipitation in 2008 was mostly attributed to two heavy precipitation months (May and June) which had 50% of the total annual precipitation ( Figure 1 ). The high precipitation intensity and large interannual variability in precipitation throughout the three years were typical in subtropical China. Mean annual air temperature did not vary much and was 22.77, 22.08, 22.71°C in 2007, 2008, and 2009, respectively. The monthly mean air temperature ranged from 11.35°C (February 2008) to 30.11°C (July 2007).

https://doi.org/10.1371/journal.pone.0041493.g001

The seasonal patterns of soil temperature in three forests were similar to the pattern of air temperature ( Figure 2a ). Among the three forests, soil in the PF was significantly warmer (22.42°C) than that in the MF (20.20°C) and the BF (20.32°C) ( Tables 2 and 3 ). No significant difference in annual mean soil temperature was found between the MF and the BF. Precipitation treatments did not change soil temperature in all three forests.

https://doi.org/10.1371/journal.pone.0041493.g002

https://doi.org/10.1371/journal.pone.0041493.t002

https://doi.org/10.1371/journal.pone.0041493.t003

Soil moisture was significantly influenced by precipitation treatments and varied among forest ecosystem types and years ( Table 2 ). Soil moisture in both the DP and AP treatments showed strong variations in all three forests ( Figure 2b ). Soil moisture was maintained at about 29% vol. in the BF and the MF, but only 20% vol. in the PF over the observation period ( Table 2 ; Figure 2b ). The DP treatment slightly increased annual mean soil moisture by approximately 11.4% compared to the AP treatment.

Effects of Precipitation Treatments on Soil Respiration, Soil Microbial Biomass and Fine Root Biomass

The soil respiration rate was significantly influenced by forest ecosystems and precipitation treatments, and the effects of precipitation treatments varied among the three forest ecosystems ( Table 2 ). Soil respiration was significantly lower in the PF (2.37 µmol CO 2 m −2 s −1 ), compared to that in the BF (3.07 µmol CO 2 m −2 s −1 ) and MF (3.15 µmol CO 2 m −2 s −1 ), averaged over three years of the experiment. The DP treatment increased mean annual soil respiration in the PF (15.4%), and did not show significant change in the BF or the MF.

The responses of soil microbial biomass and fine root biomass to precipitation treatment also varied among forest ecosystems ( Figure 3 ). Soil microbial biomass in the DP treatment increased by 19.0% and 24.0% in the MF and the PF, respectively, compared to the AP treatment ( Figure 3 ), but did not change in the BF. The DP treatment enhanced soil microbial biomass in both the wet and dry seasons in the PF, but only in the wet season in the MF. The DP treatment increased fine root biomass by 31.2% in the PF, but not in the MF and the BF ( Figure 3 ). Fine root biomass in the PF was enhanced in the dry season by the DP treatment.

Error bars are standard errors, sample size n = 6 for soil microbial biomass carbon content, sample size n = 3 for fine root biomass. Different letters in each forest denote significant difference (p<0.05) among precipitation treatments. *indicates significant difference between wet and dry seasons.

https://doi.org/10.1371/journal.pone.0041493.g003

Effects of Precipitation Treatments on the Functional Relationships of Soil Respiration with Soil Temperature and Moisture

Under both precipitation treatments and in all three forests, soil respiration responded exponentially to soil temperature and linearly to soil moisture ( Figure 4 ). The DP treatment reduced soil temperature sensitivity in the BF and the MF, but not in the PF. Soil moisture sensitivity was not influenced by the DP treatment. Since soil temperature and soil moisture interactively regulate soil respiration, we considered both soil temperature and soil moisture and fit a combination model [30] . The best regression models explained 75–93% of soil respiration variations under two precipitation treatments in three forests ( Table 4 ). The DP treatment decreased soil temperature sensitivities in the BF and the MF, but did not change soil moisture sensitivity. Basal soil respiration was enhanced under the DP treatment in both the BF and the MF. Under high temperature and heavy precipitation conditions, soil respiration under the DP treatment was lower than that under the AP treatment ( Table 4 ), but in the PF, the DP treatment did not change the functional relationship of soil respiration with soil temperature and moisture developed under the AP control.

If fitted models aren’t significantly different between in the AP and DP treatments, one single model is fitted for all data. **indicates significant relationship at α = 0.01 levels.

https://doi.org/10.1371/journal.pone.0041493.g004

https://doi.org/10.1371/journal.pone.0041493.t004

The findings from our three-year precipitation manipulation experiment provide insights into the effects of precipitation increase on forest ecosystem soil respiration in subtropical monsoon areas and may have significant implications in modeling soil respiration. First, we found that unlike in arid and semi-arid ecosystems, soil respiration in the subtropical forests showed little response to precipitation increase, even when the precipitation was doubled. Second, we proposed to differentiate two reasons of soil respiration changes in response to precipitation increase (i.e., changes due to climate factor change and/or functional change) and demonstrated that different mechanisms may lead to different responses of soil respiration to precipitation treatments in different forest sites. The DP treatment increased soil moisture, enhanced basal soil respiration, but decreased soil temperature sensitivity in the BF and MF, resulting in no change in soil respiration. The increase in soil respiration in the PF under the DP treatment was solely caused by an increase in soil moisture, as no functional change was detected. Third, the slight increase in soil respiration under the DP treatment in the PF was supported by increases in soil microbial biomass and fine root biomass. As no changes in soil microbial biomass and fine root biomass were observed in the BF treatment and only slight change in soil microbial biomass in the MF, little change in soil respiration was observed in the MF and the BF. Our findings indicate that total soil respiration might not change much in the subtropical forests if precipitation increases in the future.

Responses of Soil Respiration to Precipitation Treatments

Previous studies have indicated that the water status of an ecosystem may influence the direction of soil respiration to either reduction or increase in precipitation treatments [25] . In this study, we found 15.4% annual increase in soil respiration in the PF and no change of soil respiration in the BF and the MF ( Table 3 ). Different responses might be attributed to differences in soil condition and vegetation at these study sites. Soil in the PF contains more sand, less clay, and more gravel, and had lower ambient soil moisture content than those in the BF and the MF ( Table 1 ). Trees in the PF were younger and smaller in biomass and LAI [29] . As a result, we found that soil respiration in the PF was low, but showed a significant influence by precipitation increase. Responses of soil respiration to precipitation increase also varied among different studies. For example, the DP treatment resulted in an increase of 9.0% in soil respiration in a tallgrass prairie [10] . But a large increase of 31% in soil respiration was reported in arid and semiarid grassland with 30% increase in annual precipitation [36] . Results from a recent study indicated that soil respiration may be decreased under precipitation increase in a humid tropical forest [13] .

Functional Changes of Soil Respiration to Precipitation Treatments

Functional change of soil respiration to soil temperature/moisture under climate change is common and contributes to the responses of soil respiration in different ecosystems. A study in grasslands found that soil respiration was more sensitive to soil moisture than to soil temperature during prolonged drying cycles [52] . Ecosystems in xeric regions often possess lower soil respiration and higher soil moisture sensitivity than those in mesic regions [30] , [53] . But the response of soil respiration to soil moisture change may be different in wet subtropical forests. We found that the DP treatment did not change soil moisture sensitivity, but decreased soil temperature sensitivity significantly in the BF and the MF ecosystems ( Table 4 ). Many other studies have also found that soil respiration is insensitive to soil moisture unless that soil moisture is below levels at which metabolic activity decreases [20] , [26] , [54] .

The lower temperature sensitivities under the DP treatment here may be due to the following two reasons. 1) Enhanced soil moisture under the DP treatment might decrease soil aeration and soil oxygen concentration [14] , thus, more activation energy was needed to stimulate enzymatic rates [20] . Due to the subtropical monsoon climate, forests in the study site receive abundant heat, light, and water [55] , [56] . Therefore, soils in these wet forests are often limited by soil oxygen concentration and nutrients, especially during the hot-humid season (April-September) [47] . 2) Greater leaching of dissolved organic carbon and nutrients under the DP treatment may reduce substrate availability [15] , [57] , and result in a decline in the Q 10 values of soil respiration [8] . Previous work in this experiment has also shown that the active organic carbon, in particular particulate and light fraction organic carbon, often infiltrated to deeper soil layers with precipitation increase in the MF and BF [58] , [59] . In the PF where soil was relative drier, the DP treatment stimulated fine root biomass and microbial activity ( Figure 3 ). The greater soil microbial activity could release more nutrients from soil organic matter for fine root uptake, and increase soil respiration. The DP treatment in the BF and the MF did not stimulate soil microbe or fine root biomass, and caused little change in soil respiration in these forests.

Environmental Factor Changes Alone may Contribute to Soil Respiration Changes Under Precipitation Treatments

Environmental factor changes induced by climate change alone could have significant influences on ecosystem responses. In this study, we found that the functional response of soil respiration to soil temperature and moisture in the PF under the DP treatment was not changed compared to the AP treatment ( Table 4 ). However, increases in soil moisture under the DP treatment slightly enhanced soil respiration. A similar result was reported recently in a Mediterranean evergreen forest [37] . They found that when 27% of throughfall was excluded over three years, soil moisture was reduced by 7–10%. While the three-year throughfall exclusion did not change functional properties of the response of soil respiration to soil water content and soil temperature, soil respiration decreased by 11% due to the environmental factor change.

Limitation of the Study

In this study, we selected three typical forest ecosystems in the south of China and tested the effects of precipitation increase on soil respiration. One shortcoming of the experimental design was unreplicated forest ecosystem types. While three replicated plots were employed for each precipitation treatment (i.e. DP and AP) at each forest ecosystem site, the forest types were not replicated. Thus, the inferences regarding the response differences among forest ecosystems should be read with caution. Further studies are needed to draw rigorous conclusions regarding forest ecosystem responses using replicated forest types.

Conclusions

Using a three-year field experiment in subtropical forests in China, we demonstrated that soil respiration under the DP treatment was not changed in the BF and the MF, but slightly increased in the PF. The lower response of soil respiration was consistent with small or no change of fine root biomass and microbial biomass under the DP treatment. The different responses in the three forests were associated with both functional change and environmental factor change induced by the precipitation treatments. Changes in soil temperature sensitivity and basal soil respiration together with change in soil moisture help us understand soil respiration responses at different forest sites. The shift of soil temperature sensitivity and basal soil respiration under different precipitation regimes may have potentially significant implications for terrestrial ecosystem carbon cycling, and should be considered in terrestrial ecosystem models. Whether soil moisture sensitivity of soil respiration is changed by precipitation treatments, particularly drought, may warrant further study.

Acknowledgments

The authors are grateful to Dr. Ben Bond-Lamberty for his insightful comments and valuable suggestions. We also thank Drs. Yiqi Luo, Robert B. Jackson and Phillip Ganter for their constructive comments on an early version of this manuscript. Ms. Jennifer Cartwright provided critical editing of the manuscript.

Author Contributions

Conceived and designed the experiments: DZ DH GZ. Performed the experiments: QD J. Liu SL GC J. Li. Analyzed the data: QD DH DZ GZ. Wrote the paper: DH QD DZ GZ.

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Increased Soil Frost Versus Summer Drought as Drivers of Plant Biomass Responses to Reduced Precipitation: Results from a Globally Coordinated Field Experiment

• Published: 21 February 2018
• Volume 21 , pages 1432–1444, ( 2018 )

Cite this article

• Hugh A. L. Henry 1 ,
• Mehdi Abedi 2 ,
• Concepción L. Alados 3 ,
• Karen H. Beard 4 ,
• Lauchlan H. Fraser 5 ,
• Anke Jentsch 6 ,
• Juergen Kreyling 8 ,
• Andrew Kulmatiski 4 ,
• Eric G. Lamb 9 ,
• Wei Sun 10 ,
• Mathew R. Vankoughnett 11 ,
• Susanna Venn 12 ,
• Christiane Werner 13 ,
• Ilka Beil 8 ,
• Irmgard Blindow 8 ,
• Sven Dahlke 8 ,
• Maren Dubbert 14 ,
• Alexandra Effinger 8 ,
• Heath W. Garris 5 ,
• Maite Gartzia 3 ,
• Tobias Gebauer 14 ,
• Mohammed A. S. Arfin Khan 6 , 7 ,
• Andrey V. Malyshev 8 ,
• John Morgan 15 ,
• Charles Nock 15 ,
• Janelle P. Paulson 5 ,
• Yolanda Pueyo 3 ,
• Holly J. Stover 1 &
• Xuechen Yang 10  

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Reduced precipitation treatments often are used in field experiments to explore the effects of drought on plant productivity and species composition. However, in seasonally snow-covered regions reduced precipitation also reduces snow cover, which can increase soil frost depth, decrease minimum soil temperatures and increase soil freeze–thaw cycles. Therefore, in addition to the effects of reduced precipitation on plants via drought, freezing damage to overwintering plant tissues at or below the soil surface could further affect plant productivity and relative species abundances during the growing season. We examined the effects of both reduced rainfall (via rain-out shelters) and reduced snow cover (via snow removal) at 13 sites globally (primarily grasslands) within the framework of the International Drought Experiment, a coordinated distributed experiment. Plant cover was estimated at the species level, and aboveground biomass was quantified at the functional group level. Among sites, we observed a negative correlation between the snow removal effect on minimum soil temperature and plant biomass production the next growing season. Three sites exhibited significant rain-out shelter effects on plant productivity, but there was no correlation among sites between the rain-out shelter effect on minimum soil moisture and plant biomass. There was no interaction between snow removal and rain-out shelters for plant biomass, although these two factors only exhibited significant effects simultaneously for a single site. Overall, our results reveal that reduced snowfall, when it decreases minimum soil temperatures, can be an important component of the total effect of reduced precipitation on plant productivity.

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Acknowledgements

The authors thank Angelika Kübert for assistance with data collection.

Author information

HALH conceived of and designed the study and analyzed the data; all authors performed research and wrote the paper.

Authors and Affiliations

Department of Biology, University of Western Ontario, London, ON, Canada

Hugh A. L. Henry & Holly J. Stover

Department of Range Management, Tarbiat Modares University, Tehran, Islamic Republic of Iran

Mehdi Abedi

Instituto Pirenaico de Ecología (CSIC), Avda. Montañana 1005, 50059, Saragossa, Spain

Concepción L. Alados, Maite Gartzia & Yolanda Pueyo

Department of Wildland Resources and the Ecology Center, Utah State University, Logan, USA

Karen H. Beard & Andrew Kulmatiski

Department of Natural Resource Sciences, Thompson Rivers University, Kamloops, Canada

Lauchlan H. Fraser, Heath W. Garris & Janelle P. Paulson

Disturbance Ecology, BayCEER, University of Bayreuth, Bayreuth, Germany

Anke Jentsch & Mohammed A. S. Arfin Khan

Department of Forestry and Environmental Science, Shahjalal University of Science and Technology, Sylhet, Bangladesh

Mohammed A. S. Arfin Khan

Experimental Plant Ecology, Greifswald University, Greifswald, Germany

Juergen Kreyling, Ilka Beil, Irmgard Blindow, Sven Dahlke, Alexandra Effinger & Andrey V. Malyshev

Department of Plant Sciences, University of Saskatchewan, 51 Campus Dr., Saskatoon, SK, S7N 5A8, Canada

Eric G. Lamb

Institute of Grassland Science, Northeast Normal University, Changchun, China

Wei Sun & Xuechen Yang

Department of Biological Sciences, University of Wisconsin-Richland, Richland Center, USA

Mathew R. Vankoughnett

Centre for Integrative Ecology, School of Life and Environmental Sciences, Deakin University, 221 Burwood Highway, Burwood, VIC, 3125, Australia

Susanna Venn

Ecosystem Physiology, University of Freiburg, Breisgau, Germany

Christiane Werner

Geobotany, Faculty of Biology, University of Freiburg, Breisgau, Germany

Maren Dubbert & Tobias Gebauer

Department of Ecology, Environment and Evolution, Research Centre for Applied Alpine Ecology, La Trobe University, Bundoora, VIC, 3086, Australia

John Morgan & Charles Nock

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Henry, H.A.L., Abedi, M., Alados, C.L. et al. Increased Soil Frost Versus Summer Drought as Drivers of Plant Biomass Responses to Reduced Precipitation: Results from a Globally Coordinated Field Experiment. Ecosystems 21 , 1432–1444 (2018). https://doi.org/10.1007/s10021-018-0231-7

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Field experiments underestimate aboveground biomass response to drought

• György Kröel-Dulay   ORCID: orcid.org/0000-0002-0695-1232 1   na1 ,
• Andrea Mojzes   ORCID: orcid.org/0000-0003-2171-403X 1   na1 ,
• Katalin Szitár 2 ,
• Michael Bahn   ORCID: orcid.org/0000-0001-7482-9776 3 ,
• Péter Batáry 2 ,
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• Mark Bilton 5 ,
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• Jeffrey S. Dukes   ORCID: orcid.org/0000-0001-9482-7743 7 , 8 ,
• Marc Estiarte   ORCID: orcid.org/0000-0003-1176-8480 9 , 10 ,
• Petr Holub 11 ,
• Anke Jentsch   ORCID: orcid.org/0000-0002-2345-8300 12 ,
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• Sabine Reinsch 14 ,
• Klaus Steenberg Larsen   ORCID: orcid.org/0000-0002-1421-6182 4 ,
• Marcelo Sternberg 15 ,
• Katja Tielbörger 16 ,
• Albert Tietema 17 ,
• Sara Vicca   ORCID: orcid.org/0000-0001-9812-5837 6 &
• Josep Peñuelas   ORCID: orcid.org/0000-0002-7215-0150 9 , 10  

Nature Ecology & Evolution volume  6 ,  pages 540–545 ( 2022 ) Cite this article

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• Ecosystem ecology

A Publisher Correction to this article was published on 19 April 2022

This article has been updated

Researchers use both experiments and observations to study the impacts of climate change on ecosystems, but results from these contrasting approaches have not been systematically compared for droughts. Using a meta-analysis and accounting for potential confounding factors, we demonstrate that aboveground biomass responded only about half as much to experimentally imposed drought events as to natural droughts. Our findings indicate that experimental results may underestimate climate change impacts and highlight the need to integrate results across approaches.

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The global distribution and environmental drivers of aboveground versus belowground plant biomass

Plant responses to changing rainfall frequency and intensity

The Arctic Plant Aboveground Biomass Synthesis Dataset

To assess how climatic changes will affect ecosystems, field researchers commonly use one of two approaches: in situ observations or manipulative experiments. Observations have the advantage of being able to cover large areas and long time periods, but the links between ecosystem processes and climatic conditions are only correlational. In contrast, experiments can directly test responses to a given factor (for example, a manipulated climate variable) and isolate the effects of individual factors that often correlate with others in real-world settings. But experiments face logistical limits to their size and duration, and manipulated variables may poorly mimic natural changes or cause unwanted side effects 1 , 2 . Despite the differences between experiments and observations, few data syntheses compare the two types of studies. A recent overview of ecological responses to global change 3 found that an overwhelming majority of meta-analyses covered either experimental or observational case studies, while only 3 out of 36 assessed both types. Furthermore, global estimates of ecosystem functioning have been based on upscaling from either experiments 4 or observations 5 , but not both. The shortage of cross-domain syntheses is particularly remarkable because some comparisons have reported clear differences in results from the two approaches 6 .

In the coming decades, drought frequency and severity are projected to increase in many regions 7 , 8 . Droughts affect ecosystem functioning, including processes that influence climate 9 (for example, carbon sequestration and transpiration). Although many observational and experimental studies have assessed the effects of drought events, no synthesis study on droughts has compared results from these two approaches (but see ref. 10 for a single-site comparison). A recent review identified 564 papers studying ecological effects of droughts in the past 50 years 11 ; the majority of studies were observational. In contrast, reviews and meta-analyses of drought effects on net primary production (NPP) or aboveground biomass (AGB) focused almost exclusively on experiments, with only a single synthesis paper covering (but not comparing) both experimental and observational studies (Supplementary Note 1 ). This bias towards experimental drought studies is concerning in light of the limitations of climate change experiments, such as small spatial extent 2 and inability to replicate the full set of naturally occurring drought conditions 1 .

We compared responses of AGB to experimentally applied versus observed drought events in a systematic review using hierarchical meta-analyses. We tested for effects of potential confounding factors such as drought severity (per cent reduction in annual precipitation), drought length (years) and site aridity (the ratio of mean annual precipitation (MAP) to mean annual potential evapotranspiration (PET), MAP/PET). We first identified studies that (1) were conducted in grasslands or shrublands, (2) were conducted in natural or semi-natural systems in the field, and (3) reported aboveground NPP (ANPP), AGB or plant cover. We then excluded from our focal analysis studies from wet sites or shrublands or that estimated plant cover, because these were rare and very unequally distributed between experiments and observations. Our focal analysis included 158 data points (75 experimental and 83 observational) from 80 studies (40 experimental, 39 observational and 1 that included both types). Drought plots were compared with control plots in the experimental studies, and drought years were compared with control (non-drought) years in the observational studies. In our focal meta-analysis, we weighted the data by the number of replications. We also conducted additional meta-analyses with different weightings, and using the data that were excluded from the focal analysis, to test the robustness of our results.

The estimated mean effect of drought was 53% (95% confidence interval (CI), 16% to 90%) weaker in experimental than in observational studies, after controlling for potentially confounding factors (Fig. 1 and Supplementary Note 2 ). Drought responses increased with increasing aridity and marginally with increasing drought severity (Fig. 2 and Supplementary Note 2 ) but were not significantly affected by drought length (Supplementary Note 2 ). Interactions between study type and the other variables (site aridity, drought severity and drought length) were not significant, so we conclude that drought responses were stronger in observational than in experimental studies irrespective of site aridity and drought severity.

The results are model estimates from a meta-analytical model (Supplementary Note 2 ), presented as mean ± 95% CI ( n  = 75 for experiments and n  = 83 for observations). The pictures show a drought experiment (left) and an observational study (right), both in the sand grasslands of central Hungary. (Photos by G.K.-D.)

a , b , The lines depict relationships between lnRR and site aridity index (AI) ( a ) and drought severity ( b ) modelled using a meta-analytical model (Supplementary Note 2 ), and the shaded bands show 95% CIs ( n  = 75 for experiments (red) and n  = 83 for observations (blue)). AI was measured as MAP/PET; note that larger numbers indicate lower aridity, and 1 indicates that MAP equals PET. Drought severity was calculated as the per cent reduction in annual precipitation in drought plots (drought years in observational studies) compared with control plots (years). The circle sizes are proportional to the number of replications in the studies, which was used as a weighting factor in the meta-analysis. For the test results, see Supplementary Note 2 .

The results were very similar when we conducted an additional, variance-weighted meta-analysis on a subset of data with available estimates of variance: responses were weaker in experimental studies, at less arid sites and in less severe droughts (Supplementary Note 3 ). Furthermore, the response of AGB to drought was weaker in experiments than in observations when we conducted an unweighted meta-analysis (marginal significance; Supplementary Note 4 ) or analysed the data that were excluded from the focal analysis (wet sites, grasslands with plant cover data and shrublands; Supplementary Note 5 ). This latter finding suggests that the general pattern of weaker response in experiments holds beyond grasslands (focal dataset), even if the low number and unequal distribution of studies did not allow for a detailed analysis across a broader range of ecosystems.

The mean response to drought that we found for experiments (natural logarithm of the response ratio (lnRR), −0.28; Fig. 1 ) was similar to previous meta-analyses of drought experiments (lnRR, −0.2 to −0.28; refs. 12 , 13 , 14 ), indicating that the difference between experimental and observational studies was not due to a weaker response in experiments than in previous studies. Also, for our focal dataset, site aridity, drought severity and AGB (control) were similar in experimental and observational studies, and droughts lasted longer in experimental than in observational studies (Supplementary Note 6 ), so these factors seem unlikely to explain the weaker drought response of AGB in experiments than in observations. Publication bias was not detected for data included in the focal meta-analysis (Supplementary Note 7 ) and was therefore not considered to account for the large difference in response.

Our findings suggest that experiments considerably underestimate the effects of droughts in grasslands and shrublands. This discrepancy may occur in part because experiments typically cover small areas, and conditions in the surrounding landscape may dilute the intended treatment severity (creating an ‘island effect’ 1 , 2 ). Although we did not find a relationship between the size of drought experiments and the effect size of AGB response to drought in our focal dataset (Supplementary Note 8 ), even the largest experiments (few studies were >100 m 2 ) were much smaller than the spatial extent of natural drought events. Note that the island effect may also sometimes strengthen the treatment effect in experiments, but this usually happens as a secondary effect due to altered primary production or species composition (such as congregation or avoidance of animals 15 ). A difference between experiments and observational studies could also arise from differences in drought severity. It has been suggested that experiments tend to exaggerate drought severity relative to natural droughts 16 . However, we found that drought severity was similar across experimental and observational studies, and we used an analysis that accounted for drought severity. A potential reason for the underestimation of drought effects in experiments could be that they simulate less rain but do not control for increased evaporative demand associated with high temperatures, low humidity and clear skies. Given that droughts in reality are typically accompanied by these intensifying factors 17 , we assert that drought experiments underestimate drought effects as manifested in nature, rather than that observational studies overestimate them. In practice, using a drought severity metric that incorporates not only precipitation reduction but also variables such as temperature, humidity and cloud cover could narrow the gap between experimental and observational results. However, infrequent reporting of these variables in individual studies hinders such analyses 11 . Nevertheless, our findings that experimental and observational studies reported similar responses to changing site aridity and to changing drought severity suggest that experiments capture the major patterns of drought effects while underestimating the magnitude of the effects.

Reviews rarely compare the effects of environmental changes across study types, but from the existing comparisons, a consistent pattern emerges. Compared with experimental studies, observational studies have reported stronger effects of warming on plant phenology 6 , of fire on soil microbial biomass 18 , of disturbance on non-native plants 19 , of biological invasions on species richness 20 and of fragmentation on insect abundance 21 . Mechanisms suggested for these patterns were the same as those that may explain the differential drought effects in our study—namely, the small spatial extent 21 and incomplete representation of environmental change factors in experiments 18 , 20 . Further work is needed to test the generality of the observed discrepancies between experimental and observational results, and this should include both systematic comparison of study types across global change factors and matched case studies, where observational and experimental results come from the same sites. Yet, the common pattern across a wide range of environmental change factors listed above suggests that ecosystem manipulations, in general, tend to report weaker responses than observational studies.

Experiments have unique value even if they underestimate ecosystem responses to environmental change. Observational studies lack true controls, so observed relationships between processes and drivers are only correlational. When driving variables are correlated, as often happens in nature, the effects of individual drivers are difficult to disentangle; thus, observational studies provide limited understanding of underlying mechanisms 1 . Observations and experiments should each be used for their strengths: observations to estimate the ‘real’ net effects of climate change in realistic settings including all interacting factors, and experiments to test causation for clearly defined and experimentally reproducible driving variables and thereby obtain a mechanistic understanding. This is nicely exemplified in studies of warming effects on phenology: although warming experiments have been shown to dramatically underestimate phenological responses to warming 6 , experiments are still of great value for separating the relative effects of different factors on phenological changes in an era of warming 22 . Most importantly, our results emphasize the need to integrate results from different approaches instead of focusing on one approach and overlooking others, as seems to be common for studies of drought effects on AGB (Supplementary Note 1 ).

Reliable estimates of the magnitude of ecosystem responses to a changing climate are critically important when they are used for deriving broad-scale, sometimes global, estimates of potential change. Our results, together with those of other studies that indicate smaller responses in experimental settings than in observational studies, suggest caution when such estimates are based solely on experiments, such as when estimating change in the global stock of soil carbon on the basis of warming experiments 4 , change in global AGB on the basis of CO 2 -enrichment experiments 23 or the responses of net ecosystem exchange to changes in precipitation on the basis of precipitation experiments 24 .

We conclude that while ecosystem experiments are an invaluable tool for studying the impacts of climate change, especially to distinguish among the effects of factors that change simultaneously and to unravel the mechanisms of ecosystem responses, they may underestimate the magnitude of the effects of climate change. Thus, innovative new work that integrates experimental and observational datasets could more reliably quantify the effects of climate change on terrestrial ecosystems.

Literature search and study selection

A systematic literature search was conducted in the ISI Web of Science database for observational and experimental studies published from 1975 to 13 January 2020 using the following search terms: TOPIC: (grassland* OR prairie* OR steppe* OR shrubland* OR scrubland* OR bushland*) AND TOPIC: (drought* OR ‘dry period*’ OR ‘dry condition*’ OR ‘dry year*’ OR ‘dry spell*’) AND TOPIC: (product* OR biomass OR cover OR abundance* OR phytomass). The search was refined to include the subject categories Ecology, Environmental Sciences, Plant Sciences, Biodiversity Conservation, Multidisciplinary Sciences and Biology, and the document types Article, Review and Letter. This yielded a total of 2,187 peer-reviewed papers (Supplementary Fig. 1 ). At first, these papers were screened by title and abstract, which resulted in 197 potentially relevant full-text articles. We then examined the full text of these papers for eligibility and selected 87 studies (43 experimental, 43 observational and 1 that included both types) on the basis of the following criteria:

The research was conducted in the field, in natural or semi-natural grasslands or shrublands (for example, artificially constructed (seeded or planted) plant communities or studies using monolith transplants were excluded). We used this restriction because most reports on observational droughts are from intact ecosystems, and experiments in disturbed sites or using artificial communities would thus not be comparable to observational drought studies.

In the case of observational studies, the drought year or a multi-year drought was clearly specified by the authors (that is, we did not arbitrarily extract dry years from a long-term dataset). Please note that some observational data points are from control plots of experiments (of any kind), where the authors reported that a drought had occurred during the study period. We did not involve gradient studies that compare sites of different climates, which are sometimes referred to as ‘observational studies’.

The paper reported the amount or proportion of change in annual or growing-season precipitation (GSP) compared with control conditions. We consistently use the term ‘control’ for normal precipitation (non-drought) year or years in observational studies and for ambient precipitation (no treatment) in experimental studies hereafter. Similarly, we use the term ‘drought’ for both drought year or years in observational studies and drought treatment in experimental studies. In the case of multi-factor experiments, where precipitation reduction was combined with any other treatment (for example, warming), data from the plots receiving drought only and data from the control plots were used.

The paper contained raw data on plant production under both control and drought conditions, expressed in any of the following variables: ANPP, aboveground plant biomass (in grassland studies only) or percentage plant cover. In 79% of the studies that used ANPP as a production variable, ANPP was estimated by harvesting peak or end-of-season AGB. We therefore did not distinguish between ANPP and AGB, which are referred to as ‘biomass’ hereafter. We included the papers that reported the production of the whole plant community, or at least that of the dominant species or functional groups approximating the abundance of the whole community.

When multiple papers were published on the same experiment or natural drought event at the same study site, the most long-term study including the largest number of drought years was chosen.

In addition to the systematic literature search, we included 27 studies (9 experimental, 17 observational and 1 that included both types) meeting the above criteria from the cited references of the Web of Science records selected for our meta-analyses, and from previous meta-analyses and reviews on the topic. In total, this resulted in 114 studies (52 experimental, 60 observational and 2 that included both types; Supplementary Note 9 , Supplementary Fig. 2 and ref. 25 ).

Data compilation

Data were extracted from the text or tables, or were read from the figures using Web Plot Digitizer 26 . For each study, we collected the study site, latitude, longitude, mean annual temperature (MAT) and precipitation (MAP), study type (experimental or observational), and drought length (the number of consecutive drought years). When MAT or MAP was not documented in the paper, it was extracted from another published study conducted at the same study site (identified by site names and geographic coordinates) or from an online climate database cited in the respective paper. We also collected vegetation type—that is, grassland when it was dominated by grasses, or shrubland when the dominant species included one or more shrub species (involving communities co-dominated by grasses and shrubs). Data from the same study (that is, paper) but from different geographic locations or environmental conditions (for example, soil types, land uses or multiple levels of experimental drought) were collected as distinct data points (but see ‘ Statistical analysis ’ for how these points were handled). As a result, the 114 published papers provided 239 data points (112 experimental and 127 observational) 25 .

For the observational studies, normal precipitation year or years specified by the authors was used as the control. If it was not specified in the paper, the year immediately preceding the drought year(s) was chosen as the control. When no data from the pre-drought year were available, the year immediately following the drought year(s) (14 data points) or a multi-year period given in the paper (22 data points) was used as the control. For the experimental studies, we also collected treatment size (that is, rainout shelter area or, if it was not reported in the paper, the experimental plot size).

For the calculation of drought severity, we used yearly precipitation (YP), which was reported in a much higher number of studies than GSP. We extracted YP for both control (YP control ) and drought (YP drought ). For the observational studies, when a multi-year period was used as the control or the natural drought lasted for more than one year, precipitation values were averaged across the control or drought years, respectively. Consistently, in the case of multi-year drought experiments, YP control and YP drought were averaged across the treatment years. When only GSP was published in the paper (63 of 239 data points), we used this to obtain YP data as follows: we regarded MAP as YP control , and YP drought was calculated as YP drought  = MAP − (GSP control  − GSP drought ). From YP control and YP drought data, we calculated drought severity as follows: (YP drought  − YP control )/YP control  × 100.

For production, we compiled the mean, replication ( N ) and, if the study reported it, a variance estimate (s.d., s.e.m. or 95% CI) for both control and drought. In the case of multi-year droughts, data only from the last drought year were extracted, except in five studies (17 data points) where production data were given as an average for the drought years. When both biomass and cover data were presented in the paper, we chose biomass. For each study, we consistently considered replication as the number of the smallest independent study unit. When only the range of replications was reported in a study, we chose the smallest number.

To quantify climatic aridity for each study site, we used an aridity index (AI), calculated as the ratio of MAP and mean annual PET (AI = MAP/PET). This is a frequently used index in recent climate change research 27 , 28 . AI values were extracted from the Global Aridity Index and Potential Evapotranspiration (ET0) Climate Database v.2 for the period of 1970–2000 (aggregated on annual basis) 29 .

Because we wanted to prevent our analysis from being distorted by a strongly unequal distribution of studies between the two study types regarding some potentially important explanatory variables, we left out studies from our focal meta-analysis in three steps. First, we left out studies that were conducted at wet sites—that is, where site AI exceeded 1. The value of 1 was chosen for two reasons: above this value, the distribution of studies between the two study types was extremely uneven (22 experimental versus 2 observational data points with AI > 1) 25 , and the AI value of 1 is a bioclimatically meaningful threshold, where MAP equals PET. Second, we left out shrublands, because we had only 14 shrubland studies (out of 105 studies with AI < 1), and more importantly, only 4 of these were experimental. Finally, we left out 15 grassland studies that analysed percentage cover as the biomass proxy (instead of biomass), because 12 studies (24 data points) were observational, but only 3 (4 data points) were experimental. We thus ended up with 80 studies (39 experimental, 39 observational and 2 that included both types) and 159 data points (75 experimental and 84 observational). Please note that we used only 158 data points in our focal meta-analysis (see below).

Effect size and weighting factors

For effect size, we used lnRR, which is the most commonly used effect size metric in ecology and evolution 30 . It was calculated as ln( D / C ), where C and D are the control and drought mean of production, respectively. In most meta-analyses, effect sizes are weighted by study precision, most commonly by the inverse of study variance 31 . However, the variance estimate (s.e.m., s.d. or 95% CI) was not reported by the authors in 25% of the data points of the focal dataset. In addition, the variance-based weighting function could assign extreme weights to individual studies, resulting in the average effect size being primarily determined by a small number of studies 32 . As an alternative weighting function, replication is frequently adopted in meta-analyses 33 , 34 . We therefore weighted lnRR by replication in our focal meta-analysis. The weight associated with each lnRR value ( W i ) was calculated as W i  =  N i /∑ N i , and N i  =  N C  ×  N D /( N C  +  N D ), where N C and N D are the replication for control and drought, respectively 35 . Our focal meta-analysis included 158 data points, because the replication number ( N ) was not available for one data point of the focal dataset.

In addition to this focal replication-weighted (or N -weighted) meta-analysis, we conducted three meta-analyses to assess the robustness of our results. We performed (1) an unweighted meta-analysis for the focal dataset (159 data points), (2) a variance-weighted meta-analysis for a subset of our focal dataset where variance estimates were available (120 data points) and (3) a separate N -weighted meta-analysis for data that were left out from the focal dataset—that is, shrublands, grasslands with cover estimates and/or site AI exceeding 1 (80 data points). For the variance-weighted meta-analysis, the weights were calculated as the inverse of the pooled variance following ref. 35 . For the experimental studies in the focal dataset (75 data points), we performed an N -weighted meta-analysis to test the effect of treatment size on lnRR.

Statistical analysis

Each statistical analysis was performed in the R programming environment (v.4.1.0) 36 .

We applied meta-analytic mixed-effects models to evaluate the effects of study type and three potential confounding factors (site aridity, drought length and drought severity) on lnRR (metafor package 37 ). The three continuous variables were centred to avoid multicollinearity and to get easily interpretable parameter estimates 38 . For the full models on the focal dataset, we evaluated both the main effects of the predictors and their first-order interactions with study type. For the separate N -weighted meta-analysis on data that were left out from the focal dataset, we tested the main effect of study type only. In the N -weighted meta-analysis on the experimental studies of the focal dataset, we included treatment size as a single fixed effect. Data points from the same study received a common study ID, and study ID was treated as a random effect in all models to account for the non-independence of individual effect sizes calculated from the same study. Besides the full model in each meta-analysis, we made an information-theoretic model selection based on the Akaike information criterion corrected for small sample size by using the dredge function of the MuMIn package 39 to identify the minimum adequate model that was best supported by the data 40 . In each of the above analyses, the test assumptions were checked by visual examinations of residual diagnostic plots according to ref. 41 , and we used DHARMa package functions for testing overdispersion and homogeneity of residual variances 42 . The presence of multicollinearity among the explanatory variables was checked with variance inflation factors. Variance inflation factors were below 3 for each term in each model (except for a single interaction term (3.11); Supplementary Note 2 ), suggesting that no collinearity between predictors occurred.

For each meta-analytic model, we fitted an equivalent linear mixed-effects model using the nlme package 43 , setting the residual error to 1. We used the inverse of replication and the pooled variance as weights in the N -weighted and variance-weighted models, respectively. In this way, we could extract analysis of variance tables showing the significance test of each fixed-effect term, and we computed R 2 values as a measure of model fit according to ref. 44 using the r2glmm package 45 .

For the focal dataset, we tested whether experimental and observational studies differed in average site aridity, drought length, drought severity and AGB. For site aridity, we applied a beta regression with a logit link function, using the glmmTMB package 46 . The difference in drought length between experimental and observational studies was tested with a generalized mixed-effects model with a Poisson distribution and a log link function (lme4 package 47 ). Linear mixed-effects models were used to assess the difference in drought severity and in AGB between the two study types (nlme package 43 ). For the comparison of AGB, we used the control mean of each data point and converted the different units of biomass reported in the papers into g m −2 . In each analysis, we used study ID as a random effect.

In addition, we considered two other potential confounding factors: plant species richness, which often positively affects primary productivity, and dominant life form (annual versus perennial), because annual-dominated ecosystems may be less resistant to drought than those dominated by herbaceous perennials 48 . However, we found very limited species richness data; it was included in only 16 studies (20% of studies). Furthermore, these data were estimated at various spatial scales (ranging from 0.04 to 10,000 m 2 ) depending on the study. We therefore could not include species richness in the analysis as a potential confounding factor or even reliably compare this variable between the two study types in a separate analysis. Regarding dominant life form, the overriding dominance of perennial grasslands in our focal dataset (70 of the 80 studies) did not allow us to include this variable in our analysis.

We assessed whether publication bias could be detected for the data included in the focal meta-analysis, and for experimental and observational studies separately, by using two frequently used methods. First, we performed a file-drawer analysis with the Rosenberg method 49 by calculating the number of studies averaging null results that would have to be added to our set of observed outcomes to reduce the combined P value to 0.05. Second, we assessed asymmetry in funnel plots on the basis of Egger’s regression test 50 . Both analyses were performed using the metafor package 37 .

Reporting Summary

Further information on research design is available in the Nature Research Reporting Summary linked to this article.

Data availability

The data that support the findings of this study are available in figshare 25 with the identifier https://doi.org/10.6084/m9.figshare.17881073 . The AI data were extracted from Global Aridity Index and Potential Evapotranspiration (ET0) Climate Database v.2, which is available in figshare 29 with the identifier https://doi.org/10.6084/m9.figshare.7504448.v3 .

Code availability

The computer code (R scripts) of the analyses is available in figshare 25 with the identifier https://doi.org/10.6084/m9.figshare.17881073 .

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A Correction to this paper has been published: https://doi.org/10.1038/s41559-022-01767-2

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Acknowledgements

We thank EU CLIMMANI COST Action (ES1308; PI, C.B.) for supporting all co-authors and initiating discussion on the topic. G.K.-D. and A.M. received funding from the National Research, Development and Innovation Fund (NRDI Fund) of Hungary (grant nos 120844 (A.M.), 112576 and 129068 (G.K.-D.)). J.P. was supported by Fundación Ramon Areces grant ELEMENTAL-CLIMATE and the European Research Council grant ERC-SyG-2013-610028. H.J.D.B. was funded through AnaEE-Flanders project no. I001921N. A.J. was funded by the German Federal Ministry of Education and Research FKZ 031B0027C.

Author information

These authors contributed equally: György Kröel-Dulay, Andrea Mojzes.

Authors and Affiliations

Institute of Ecology and Botany, Centre for Ecological Research, Vácrátót, Hungary

György Kröel-Dulay & Andrea Mojzes

‘Lendület’ Landscape and Conservation Ecology, Institute of Ecology and Botany, Centre for Ecological Research, Vácrátót, Hungary

Katalin Szitár & Péter Batáry

Department of Ecology, University of Innsbruck, Innsbruck, Austria

Michael Bahn

Department of Geosciences and Natural Resource Management, University of Copenhagen, Frederiksberg, Denmark

Claus Beier, Inger Kappel Schmidt & Klaus Steenberg Larsen

Namibia University of Science and Technology, Windhoek, Namibia

Mark Bilton

Plants and Ecosystems (PLECO), Department of Biology, University of Antwerp, Wilrijk, Belgium

Hans J. De Boeck & Sara Vicca

Department of Forestry and Natural Resources, Purdue University, West Lafayette, IN, USA

Jeffrey S. Dukes

Department of Biological Sciences, Purdue University, West Lafayette, IN, USA

CSIC, Global Ecology Unit CREAF-CSIC-UAB, Bellaterra, Spain

Marc Estiarte & Josep Peñuelas

CREAF, Cerdanyola del Vallès, Spain

Global Change Research Institute of the Czech Academy of Sciences, Brno, Czech Republic

Disturbance Ecology, Bayreuth Center of Ecology and Environmental Research, University of Bayreuth, Bayreuth, Germany

Anke Jentsch

Experimental Plant Ecology, University of Greifswald, Greifswald, Germany

Juergen Kreyling

UK Centre for Ecology & Hydrology, Bangor, UK

Sabine Reinsch

School of Plant Sciences and Food Security, Faculty of Life Sciences, Tel Aviv University, Tel Aviv, Israel

Marcelo Sternberg

Plant Ecology Group, University of Tübingen, Tübingen, Germany

Katja Tielbörger

Institute for Biodiversity and Ecosystem Dynamics (IBED), Ecosystem and Landscape Dynamics (ELD), University of Amsterdam, Amsterdam, the Netherlands

Albert Tietema

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G.K.-D. conceived the research through discussion with all co-authors. A.M., K.S. and G.K.-D. compiled the dataset. K.S. conducted the data analysis. G.K.-D. and A.M. wrote the paper with substantial inputs from all co-authors.

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Kröel-Dulay, G., Mojzes, A., Szitár, K. et al. Field experiments underestimate aboveground biomass response to drought. Nat Ecol Evol 6 , 540–545 (2022). https://doi.org/10.1038/s41559-022-01685-3

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Nitrogen fertilization alleviates barley ( hordeum vulgare l.) waterlogging.

1. Introduction

2. materials and methods, 2.1. plant materials and fertiliser treatments, 2.2. plant recovery indicators, 2.3. shoot biomass, 2.4. grain yield and yield components, 2.5. data analysis, 3.2. yield components, 3.3. shoot biomass, 3.4. fertiliser at different crop growth stages influences plant recovery, 4. discussion, 4.1. crop genotype dictates relative waterlogging impacts, 4.2. yield penalty under waterlogging primarily governed by spike number, 4.3. effects of waterlogging on grain weight, 4.4. impacts of different fertiliser application methods on alleviating waterlogging damage, 4.5. future directions, 5. conclusions, supplementary materials, author contributions, data availability statement, acknowledgments, conflicts of interest.

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Share and Cite

Chen, J.; Zhao, C.; Harrison, M.T.; Zhou, M. Nitrogen Fertilization Alleviates Barley ( Hordeum vulgare L.) Waterlogging. Agronomy 2024 , 14 , 1712. https://doi.org/10.3390/agronomy14081712

Chen J, Zhao C, Harrison MT, Zhou M. Nitrogen Fertilization Alleviates Barley ( Hordeum vulgare L.) Waterlogging. Agronomy . 2024; 14(8):1712. https://doi.org/10.3390/agronomy14081712

Chen, Jianbo, Chenchen Zhao, Matthew Tom Harrison, and Meixue Zhou. 2024. "Nitrogen Fertilization Alleviates Barley ( Hordeum vulgare L.) Waterlogging" Agronomy 14, no. 8: 1712. https://doi.org/10.3390/agronomy14081712

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UW Research and Extension Center to Host Field Day and Precision Ag Expo Aug. 22

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Institutional Communications Bureau of Mines Building, Room 137 Laramie, WY 82071 Phone: (307) 766-2929 Email:   [email protected]

Published August 08, 2024

The University of Wyoming’s Wyoming Agricultural Experiment Station will host a field day at the James C. Hageman Sustainable Agriculture Research and Extension Center (SAREC) near Lingle Wednesday, Aug. 22.

The field day will be integrated with a precision agriculture exposition, hosted in collaboration with Eastern Wyoming College.

The field day and expo are free and open to the public. Registration will open at 9 a.m.

From 9:30 a.m. to noon, UW researchers will present their current investigations during a field tour of SAREC grounds. Participants also can peruse scientific posters created by UW researchers.

“I like to think that all producers in the area would benefit from seeing some of the current technology and viewing for themselves some of the actual research that’s being conducted in crop management and variety trials,” says Steve Paisley, director of SAREC.

A catered lunch will be served at noon, followed by a keynote speech by Ron Rabou, an organic farmer from southeast Wyoming, who will discuss innovation in agriculture.

After Rabou’s presentation, the precision ag expo will begin, offering participants the opportunity to learn about precision ag equipment.

Precision ag uses new technologies to improve efficiency, allowing producers to manage exactly how materials, such as seeds, herbicides or water, are used. These technologies include tractors with autosteer and GPS capabilities and pivot irrigation systems that can adjust how much water is applied to different parts of the field.

Throughout the day, participants will have a chance to examine precision ag equipment from local Torrington dealerships. From 2-5 p.m., dealership vendors will perform equipment demonstrations, including tractor field trials and commercial drone herbicide applications.

Following the precision ag field demonstrations, a closing reception will be held at 5 p.m., allowing for follow-up questions and discussion.

The expo is part of a grant from the Wyoming Innovation Partnership, which is designed to train Wyoming youth in skills that will allow them to be successful in Wyoming.

Attendees are asked to RSVP for the field day and expo by emailing [email protected] or calling (307) 837-2000 no later than Friday, Aug. 16.

About the Wyoming Agricultural Experiment Station

The Wyoming Agricultural Experiment Station (WAES) is the research unit of UW’s College of Agriculture, Life Sciences and Natural Resources. WAES conducts fundamental and applied research related to agricultural production, natural resource stewardship, economic development and community well-being.

Founded in 1891, WAES remains committed to addressing the current and future needs of the state, region, nation and world through rigorous scientific investigation. In addition to supporting research on campus, WAES operates research and extension centers in Laramie, Lingle, Powell and Sheridan.

To learn more, call (307) 766-3667 or visit www.uwyo.edu/uwexpstn .

ORIGINAL RESEARCH article

Enhancing nitrogen management in corn silage: insights from field-level nutrient use indicators.

• 1 Department of Animal Science, Cornell University, Ithaca, NY, United States
• 2 PRO-DAIRY, Department of Animal Science, Cornell University, Ithaca, NY, United States

Corn ( Zea mays L.) silage is an important feed ingredient in dairy cow diets in New York (NY). Improving corn nitrogen (N) management will help increase farm profitability while reducing environmental impacts from N losses. The objectives of this study were to (1) characterize field-based N balances and other N use indicators for corn silage, and (2) describe major contributors to high balances and inefficiencies as a first step to understand potential opportunities to improve N management. Field-level N balances (N supply – N uptake) and associated N use indicators were derived for 994 field observations across eight NY dairy farms and 5 years. Available and total N balances per ha, which differed only in the fraction of manure N accounted for (plant-available N or total N, respectively), yield-scaled N balances, and N uptake/N supply were calculated. The median balance across all fields was 111 kg N ha −1 for available N and 245 kg N ha −1 for total N. Median yield-scaled available and total N balances were 2.7 and 6.0 kg N Mg −1 , respectively. Median N uptake/N supply was 0.60 for available N and 0.41 for total N. Differences in N use indicators were larger among farms than among years within a farm. The amount of N supply greatly influenced N use indicators, manure N supply explaining the largest portion of the variability. At the whole-farm level, balances per ha were positively related to farm’s animal density and impacted by farm crop rotations and within-farm allocation of manure N. We conclude that farms have opportunities to improve upon N management for corn by adjusting N supply based on realistically attainable yield, fully crediting manure and sod N contributions, improving manure inorganic N utilization efficiency, optimizing animal density, and/or exporting manure. Future work is needed to identify feasible ranges for field-level N balances and incentivize the implementation of this assessment through adaptive nutrient management policies.

1 Introduction

Nitrogen (N) is a critical element for agricultural productivity. Despite improvements over the last few decades, N use efficiency in agricultural systems in many cases remains low ( Lassaletta et al., 2014 ; Zhang et al., 2015 ). Optimizing N use is particularly challenging in livestock systems given the diversity of N sources producers manage for crop production (manure, legume N fixation, fertilizer) and the uncertainty in nutrient availability. When N inputs surpass plant N needs, it can lead to proportionally larger reactive N losses through volatilization, denitrification, leaching or runoff. These losses are associated with environmental challenges such as groundwater contamination, eutrophication of freshwater, and global warming ( Galloway et al., 2003 ; Lassaletta et al., 2016 ). On the contrary, sustained N supply below crop N requirements may impact crop yield and quality ( Sadeghpour et al., 2017 ), reduce the uptake efficiency of other nutrients, and lead to soil organic N mining, compromising soil quality ( Campbell and Zentner, 1993 ).

With the intensification of dairy systems across the United States over the last decades, corn silage use in dairy cow diets has grown notably ( Martin et al., 2017 ; Powell et al., 2017 ). New York, which ranks 5th in milk production in the United States, has not been an exception to this trend. In 2022, 416,826 ha of corn were planted of which 43% were harvested as silage ( USDA, 2023 ). Factors driving the increase in corn silage production include a single harvest system, greater yields and higher energy content compared to other forages ( Powell and Rotz, 2015 ). With the increase in land in corn production by dairies in the Northeast United States, N fertilizer use tends to increase as well ( Ros et al., 2023 ). As large use of external N inputs is linked with higher inefficiencies and reductions in whole-farm N use efficiency of dairies ( Martin et al., 2017 ; Powell et al., 2017 ), it is imperative to develop tools that aid farmers with better N management.

One strategy to monitor and improve nutrient management in agricultural systems is the use of nutrient balances ( Oenema et al., 2003 ; Sharara et al., 2022 ). Balances have been used to track nutrient use at the regional ( Swink et al., 2011 ; Zhang et al., 2019 ; Godber et al., 2024 ), whole-farm ( Cela et al., 2015 ; Pearce and Maguire, 2020 ), and field level ( Tenorio et al., 2020 ). Balances are not necessarily an indicator of nutrient losses to the environment. However, surplus N at the field level has been shown to be associated with emissions of nitrous oxide, a global warming gas ( Grassini and Cassman, 2012 ; Eagle et al., 2020 ; Maaz et al., 2021 ), surface N runoff, and N leaching ( Zhao et al., 2016 ; Sadeghpour et al., 2017 ; McLellan et al., 2018 ; Hanrahan et al., 2019 ; Tamagno et al., 2022 ). Ideal field-level N balances are positive, but not excessively large, in order to replenish soil organic N, support the growth of unharvested plant material, and account for nutrients not recovered by plants while growing.

Regulations for Concentrated Animal Feeding Operations (CAFOs) in New York (NY) require producers to have a Comprehensive Nutrient Management Plan (CNMP) and follow land-grant university guidelines for N management in field crops ( Ketterings and Workman, 2023 ). In 2013, an Adaptive Nitrogen Management process was added, allowing producers to apply N at a higher rate than recommended as long as field-based yield records are obtained and an environmental N use efficiency assessment is conducted to assess if the extra N was warranted ( Ketterings et al., 2023 ). Currently, producers who opt for Adaptive Nitrogen Management for a field, can select one of four alternatives to evaluate N use. Options include taking corn stalk nitrate test (CSNT) samples, conducting N rate studies, or putting in test strips to compare the higher rate against the recommended one ( Ketterings et al., 2023 ). A field N balance option was added for fields planted to crops other than corn. However, if feasible balances can be determined, this option can also be added for corn as an alternative to CSNTs that can be spatially variable and labor-intensive. Field-balances can provide farmers with useful information at a scale where improvements can be implemented ( Sela et al., 2019 ; Van Leeuwen et al., 2019 ; Tenorio et al., 2020 ).

The objectives of this study are to (1) characterize current N use indicators for corn silage production across eight NY dairy farms, including N balance per ha, yield-scaled N balance, and N uptake/N supply, and (2) describe major drivers of these indicators, as a first step to understand potential opportunities to improve N management for corn silage and relevant aspects to consider when defining feasible N balances.

2 Materials and methods

2.1 farm characteristics, data collection and quality.

Data were collected for eight dairy farms ( Table 1 ), two each in eastern, northern, central, and western NY. Farms 2 and 5 were medium CAFOs (between 300 and 700 mature cows) and the rest were large CAFOs (more than 700 mature cows). General farm information was obtained from the input sheets of the Cornell University Nutrient Management Spear Program whole farm nutrient mass balance project (Cornell NMSP, 2023). For individual corn fields, between 2 and 4 years of data were collected, depending on data availability and quality (growing seasons between 2018 and 2022). Information was retrieved from individual farm records, the farm’s CNMPs, and interviews with farm owners and consultants. Only fields with corn silage as the harvested crop were included (double-cropped fields were excluded due to uncertainty in yield data for winter cereals harvested for forage). Data were checked thoroughly for quality. Fields with incomplete or uncertain/unknown manure application information, and a small number of fields with unrealistic yield or balance data were excluded from the dataset. The latter included four fields with yields equal or below 11.2 Mg ha −1 (5 tons acre −1 ) and two fields with N balances smaller than −84 kg ha −1 (−75 lbs acre −1 ). Individual meetings were held with farm owners and consultants to discuss data reliability and preliminary results. After data quality checks were completed, 994 field*year observations (record for an individual field in a specific year) were kept in the dataset (10,048 ha) from 560 individual fields (5,382 ha). Aggregated rainfall during the most relevant months of the cropping season (April 1st to August 31st) and annual average temperature were retrieved from CLIMOD 2 ( Supplementary Figure S1 ; Northeast Regional Climate Center, 2024 ).

Table 1 . Characteristics and data collection aspects for eight New York farms analyzed in the present study.

2.2 Estimation of N use indicators

Field-level N use indicators were derived following the approach described in Berlingeri et al. (2021) . Briefly, N removal in individual fields was calculated as the product of N uptake intensity (kg N Mg silage −1 at 35% dry matter), and yield values for individual fields (Mg silage ha −1 at 35% dry matter). Yield was obtained by the farmers using yield monitor systems on choppers. Raw yield monitor data were processed through a standardized, semi-automated data cleaning protocol ( Kharel et al., 2018 ) prior to determining yield per field. Nitrogen uptake intensity was adjusted based on hybrid relative maturity (4.3 kg N Mg −1 for hybrids with ≤95 days to maturity, and 4.1 kg N Mg −1 for hybrids with >95 days to maturity) as determined using corn variety testing results from NY ( Berlingeri et al., 2021 ). When no information was available about hybrids planted in a field, an average of 4.2 kg N Mg −1 was used.

Available and total N supply from different sources in each cropping season (N supply) differed only in the fraction of N in manure considered and were estimated as follows Equations 1 , 2 :

Where, N Soil  = endogenous soil N supply, N Sod  = N supply from legumes in sod crop (grass, legumes or grass-legume mixes for forage production) prior to corn, N Past_manure  = N supply from manure applications in the two previous cropping seasons, N Current_manure_av  = plant-available N supply from manure applications during the current cropping season, N Current_manure_to  = total N supply from manure applications during the current cropping season, N Fertilizer  = N supply with inorganic fertilizer application, N Cover_crop  = N supply from cover crops prior to corn, N Soybean  = N supply from soybean crop prior to corn, and N Idle  = additional N supply in corn fields preceded by no other crops.

Manure available N supply (N Current_manure_av ) and manure total N supply (N Current_manure_to ) were defined from applications during the current cropping season assuming: (1) N availability as impacted by organic N mineralization rates, affected by timing and method of application (available inorganic N), and (2) no N losses (total N). Manure N credits were calculated based on farmer records for manure nutrient content, rates, timing, and method of application, and N availability factors defined in land-grant university guidelines ( Ketterings and Workman, 2023 ). Nitrogen supply from past manure applications included credits from amendments applied up to two prior cropping seasons. Book values for N supply from soil, sod, cover crop, soybean and idle land were also taken from land-grant university guidelines ( Ketterings and Workman, 2023 ). Soil N supply values were estimated by previous research based on soil organic N mineralization during the cropping season and are specific for each soil type, ranging between 56 and 90 kg N ha −1 . Sod N contributions vary according to the proportion of legumes in the sod mix at the time of termination (prior to corn planting) as reported by farmers, and rotation stage (first, second, or third year of corn after sod), resulting in values ranging between 9 and 185 kg N ha −1 . An additional N supply of 33 kg N ha −1 was added to observations where corn planting was preceded by a cover crop, soybean crop or no crops (idle land). For the purpose of nutrient application accounting, cropping seasons ranged between September of the calendar year prior to corn planting, and August of the calendar year in which corn was planted (fall and spring).

A total of six N use indicators were derived Equations 3–8 :

Each observation in the database was associated with a particular soil type, soil management group (SMG), soil N uptake efficiency category, and drainage class as defined in land-grant university guidelines ( Ketterings and Workman, 2023 ). Briefly, the database included mineral soils from SMGs 1 through 5. The SMGs aggregate fields based on soil texture and parent material. Soil N uptake efficiency categories are soil type and drainage dependent and represents the fraction of the inorganic N applied with external N sources that can be recovered by plants under best management practices. Categories include ≤60, 65, 70, and 75%. For drainage, five classes were considered: poorly or very poorly drained (V + P), somewhat poorly drained (S), moderately drained (M), and well or excessively drained (W + E).

2.3 Statistical analysis

General descriptive statistics were derived for each of the N use indicators considering the entire database, specific groups of fields, or individual farms. Relative frequency distributions were used to initially assess the variability of N supply, yield, N uptake, and N use indicators.

The relationship between N use indicators and potential explanatory variables were investigated using linear mixed effects models in R (lmer function from lme4 package) ( Bates et al., 2015 ). This approach utilizes restricted maximum likelihood to fit the models and helps confidently analyze unbalanced datasets with correlated observations. This was particularly relevant given fields belonged to specific farms, some fields were assessed multiple years, and different operations and years contributed in different proportions to the overall dataset. Field, farm, year, and the farm*year interaction were initially considered random effects in all models and dropped if the variance explained was zero or nearly zero as defined by singularity tests run by the lmer function. Intercept-only models were initially fitted to analyze variables intercept values, and relevance of random effects. Then, a group of parameters of interest were tested as fixed effects, one at a time, for their relationship with the different N use indicators and associated variables. Data transformations (natural logarithm and square root) were applied to yield-scaled N balance and N supply/N uptake to ensure normality of model residuals. For yield-scaled N balance, a constant factor equal to the sum of the minimum value in the dataset plus one was added to all observations to ensure the distribution had no negative values before applying the transformations. After fitting the models, the data were evaluated for assumptions of homogeneity of variances, and normality of residuals using histograms, residuals vs. fitted plots, and QQ-plots. Type III Satterthwaite corrected p values were calculated using lmerTest package in R ( Kuznetsova et al., 2020 ) to define the significance of intercepts and fixed effects in the models. For each model, the goodness of fit was estimated through the coefficient of determination ( R 2 ). Marginal R 2 ( R 2 m, portion of the variance explained by fixed effects), and conditional R 2 ( R 2 c, portion of the variance explained by the entire model, combining fixed and random effects) were calculated in R using the r.squared.GLMM function of the MuMIn package ( Nakagawa and Schielzeth, 2013 ; Nakagawa et al., 2017 ; Bartoń, 2023 ). Variables tested as fixed effects that had a significant relationship with the response ( p  < 0.05) and showed a R 2 m equal or greater than 0.05 were considered relevant for the analysis.

The effects of categorical variables such as SMG, soil N uptake efficiency categories, soil drainage classes, or rotation stage, on N use indicators or their drivers were examined through an analysis of variance (ANOVA) on the fitted linear mixed model (lmerTest package) ( Kuznetsova et al., 2020 ). If significant, post-hoc contrasts were conducted with the emmeans function (emmeans package, R) to detect differences between estimated marginal means for different levels of the categorical variables using Tukey’s adjustment ( Lenth et al., 2023 ).

Linear models were fitted with the lm function in R (R stats package, R Core Team, 2021 ), to model the relationship between farm animal density and farm-level area-weighted averages for available N balance, total N balance, total manure N supply, and fertilizer N supply. The coefficient of determination ( R 2 ) was estimated as a measure of model fit.

3.1 Database characteristics

Median yield across all observations was 40.6 Mg ha −1 , with an area-weighted mean of 43.4 Mg ha −1 ( Figure 1 ). Nitrogen uptake ranged from 54 to 309 kg N ha −1 , with a median of 170 kg N ha −1 (data not shown). Available N supply ranged from 106 to 613 kg N ha −1 , with a median of 278 kg N ha −1 , while total N supply ranged from 106 to 958 kg N ha −1 , with a median of 426 kg N ha −1 . For available N supply, the largest source of N was manure, with 87 kg N ha −1 on average across all observations analyzed (46 and 41 kg N ha −1 from organic and inorganic fractions, respectively), followed by soil N supply (82 kg N ha −1 ), and fertilizer (71 kg N ha −1 ) ( Figure 1D ).

Figure 1 . Relative frequency distributions for available nitrogen (N) supply (A) , total N supply (B) , and corn silage yield (C) , and area-weighted average N supply from multiple sources across farms and years (D) . In (D) , black bolded numbers on top of the bars represent the aggregated N supply from multiple N sources (individual stacked bars). In the same graph, bars with values equal or smaller than 20 kg N ha −1 are not labeled. x ¯ = area-weighted average, IE = intercept estimate reported by intercept-only linear mixed effect model, σ = area-weighted standard deviation, S = skewness estimated with Pearson’s second coefficient of skewness.

Fertilizer N was applied in 952 observations (96% of the database). Seventy-eight percent of the observations received manure applications during the current cropping season ( n  = 772) and in 46% ( n  = 457) of all data points, manure was incorporated or injected in the spring prior to corn planting, resulting in manure inorganic N credits. On average, 34% of the organic N from manure was estimated as available while for inorganic N, an area-weighted average of 37% was plant-available across all fields, farms, and years. Forty-seven percent of observations had N contributions from previous sod ( n  = 469). From these observations, 32% corresponded to first year corn silage after sod (COS1, with sod N credits between 93 and 185 kg N ha −1 ), 36% to data points in their second year of corn after sod (COS2, sod N credits between 20 and 40 kg N ha −1 ), and 32% in their third year after corn (sod N credits between 9 and 17 kg N ha −1 ). A total of 243 observations (24% of the database) had N contributions from cover crops (33 kg N ha −1 ). Across all farms, N supply attributed to residual N contributions from soybeans and idle land were low, given dairy farms do not typically grow soybean in rotation and little land is left idle.

3.2 Drivers of yield

Random effects explained 57% of the variability in yield in intercept-only models ( Table 2 ). The factor farm explained the largest portion (22%), followed by the farm*year interaction (19%). Field explained 16% and year 0% of the variability observed in yield. Similarly, SMG, soil N uptake efficiency categories, and drainage classes explained small portions of the changes in yield across observations ( R 2 m = 0.02). However, yield varied significantly across levels in these groups. Fields with soil type(s) in SMG 1, characterized by fine- and medium to fine-textured soils developed from lake sediments, showed significantly lower yields than fields with soil types in other SMGs ( Figure 2 ). Soils for which the current N guidelines for field crops in NY assign higher N uptake efficiency rates (75 and 70%), showed significantly higher yields than those with lower uptake efficiency (≤60%). Fields under the category “well or excessively drained” (W + E), exhibited significantly higher yields than those in other drainage categories.

Table 2 . Summary statistics for different linear mixed models fitted to explore the relationship between yield, and nitrogen (N) inputs, soil characteristics and model random effects (field, farm, year and farm*year).

Figure 2 . Corn silage yield for different soil management groups (A) , soil nitrogen (N) uptake efficiency categories (B) , soil drainage classes (C) , and years of data collection (D) . Gray dots represent individual field*year observations. Numbers above the box plots (blue) indicate number of observations in each category, and numbers below (green) indicate marginal means estimated by linear mixed effect model outputs. Estimated marginal means for independent variables within each panel that share the same letter, are not significantly different at p  = 0.05. V + P, very poorly or poorly drained; S, somewhat poorly drained; M, moderately drained; W + E, well or excessively drained.

Manure available N supply and total N supply showed the largest explanatory power for yield ( R 2 m = 0.05), followed by available N supply, and total manure N ( R 2 m = 0.04). Observations with manure applications ( n  = 772) had an estimated marginal mean 5.5 Mg ha −1 higher than fields with no manure applications ( n  = 222) ( p  < 0.05), with an area-weighted average difference of 3.0 Mg ha −1 . Observations with manure applications also showed average available and total N supply 60 kg N ha −1 and 255 kg N ha −1 higher, respectively, than observations with no manure applications. Although sod N showed low explanatory power for changes in yield ( R 2 m = 0.01), fields with sod N credits had a significantly lower estimated marginal mean for yield compared to fields without sod grown in the most recent 3 years prior to corn (40.8 vs. 42.1 Mg ha −1 ).

3.3 Nitrogen use indicators

Available N balances showed a median and area-weighted average of 111 kg N ha −1 ( Figure 3A ). Total N balances showed a median of 245 kg N ha −1 , and an area-weighted average of 268 kg N ha −1 ( Figure 3B ). The median difference between available and total N balances was 134 kg N ha −1 , while the difference between area-weighted averages was 157 kg N ha −1 . Median values for yield-scaled available and total N balances were 2.9 and 6.6 kg N Mg −1 , respectively ( Figures 3C , D ). For N uptake/available N supply the median value was 0.60 ( Figure 3E ) and for N uptake/total N supply, 0.47 ( Figure 3F ). Not considering soil N contributions, available N balances ranged from −158 to 309 kg N ha −1 , with a median of 29 kg N ha −1 (data not shown). Total N balances had a median of 163 kg N ha −1 , and ranged from −150 kg to 735 kg N ha −1 . Medians for N uptake/available N supply and N uptake/total N supply were 0.85 and 0.51, respectively, when not including soil N supply. All variables showed different degrees of positive skewness due a small subset of observations with high values.

Figure 3 . Relative frequency distributions for available nitrogen (N) balances per ha (A) , total N balances per ha (B) , yield-scaled available N balances (C) , yield-scaled total N balances (D) , N uptake/available N supply (E) , and N uptake/total N supply (F) , for all observations across farms and years. x ¯ = Area-weighted average, IE = intercept estimate reported by intercept-only linear mixed effect model, σ = area-weighted standard deviation, S = skewness estimated with pearson’s second coefficient of skewness.

A total of 77 observations had negative balances (8% of the total database), and 137 observations (14% of the database) showed N uptake/available N supply above 90%. Thirteen, 13, 28, 24, 10, 3, 2, and 7% of these high efficiency observations were from Farms 1–8, respectively. Seventy-eight percent of the observations with N uptake/available N supply above 90% belonged to Farms 1–4, the operations with the lowest animal densities (AU ha −1 , Table 1 ). Overall, available and total N supply for these fields were 84 and 149 kg N ha −1 lower, respectively, than the average for the database, with reduced N contributions from fertilizer, manure, and sod (similar soil N supply, and past manure application N credits). This resulted in an average available N balance of −5 kg N ha −1 , and a total N balance of 87 kg N ha −1 . The high efficiency data points were similarly distributed across soil drainage and soil N uptake efficiency categories, compared to the entire database, but had a larger proportion of soil types in SMG 4 (50% vs. 24%). Soil management group 4 clusters coarse- to very coarse-textured soils, formed from gravelly or sandy glacial outwash or glacial lake beach ridges or deltas ( Ketterings and Workman, 2023 ). Observations with N uptake/available N supply >90% also showed an average yield of 50.8 Mg ha −1 , 7.4 Mg ha −1 higher than the average for the full database.

3.4 Farm, year, and field

Year showed the smallest explanatory power across all N use indicators (5 to 7% of the total variability, Table 3 ). For available N balances per ha, farm and farm*year explained the largest proportions of the variability. For yield-scaled available N balance, and N uptake/available N supply, farm*year explained the largest portion of the variability, followed by farm and field. Moreover, for total N balances per ha, yield-scaled total N balance and N uptake/total N supply, farm was the largest driver, followed by field. Finally, unexplained field-to-field differences (residual, almost 50% of the variance for all models) were as relevant as all other random factors considered together.

Table 3 . Intercept estimates, standard errors, p values and variance components of random effects (field, farm, year, farm*year) from intercept-only linear mixed models, for different nitrogen (N) use indicators.

3.5 N uptake vs. N supply

Marginal R 2 results were larger for models including N supply than N uptake, across all N use indicators ( Table 4 ). The difference was particularly relevant for available and total N balances per ha ( R 2 m of 0.75 and 0.94 for N supply in available and total balances per ha, respectively, compared to 0.08 and 0, for N uptake) ( Figure 4 ). Marginal R 2 for models including N uptake as the only predictor were larger for yield-scaled N balances, and N uptake/N supply compared to balances per ha.

Table 4 . Summary statistics for different linear mixed models fitted to explore the relationship between each nitrogen (N) use indicator and N supply and N uptake.

Figure 4 . Available (A) and total (B) nitrogen (N) balances per ha, as related to N uptake and N supply (available or total). Each data point represents N uptake or N supply for a field*year observation in the database. Lines and equations correspond to outputs from linear mixed effect models. R 2 m, marginal coefficient of determination. NS, model not significant.

3.6 Impact of N supply sources and soil characteristics on N indicators

Available manure organic N explained 15% of the variability for available N balances per ha, 5% for yield-scale available N balances, and 6% for N uptake/available N supply ( Table 5 ). Total manure organic N supply explained 88% of the variability for total N balances per ha. Manure N contribution was the single largest driver of changes in all indicators modeled among the N sources analyzed. Estimates for model coefficients were positive for all balances, indicating increases in manure N supply, resulted in higher balances. For N uptake/available N supply, the relationship was negative. Congruent with this finding, fields with manure applications showed significantly higher available N balances per ha than fields receiving no manure (estimated marginal means of 110 kg N ha −1 vs. 82 kg N ha −1 , respectively). Similarly, available manure inorganic N explained 12 and 65% of the variability in available and total balances per ha. Balances increased with higher manure inorganic N supply. Fertilizer N application explained 5 to 6% of the variability in available balances per ha, yield-scaled available N balance and N uptake/available N supply. Similar to manure N, higher N supply with fertilizer resulted in a higher available N balance and yield-scaled N balance, and lower N uptake/N supply.

Table 5 . Summary statistics for different linear mixed models fitted to explore the relationship between nitrogen (N) use indicators, and N inputs, and soil characteristics.

There was a negative relationship between fertilizer N and manure available N application (coefficient = −0.2, R 2 m = 0.06) ( Table 6 ). On average across all farms and years, fertilizer N applications were reduced by 0.2 units, with a one unit increase in available N from manure (combining organic and inorganic N). Only small portions of the variability in fertilizer N application were explained by changes in N supply from past manure applications ( R 2 m = 0.01) or sod credits ( R 2 m = 0.01). However, fields in the first year of corn silage after sod (COS1) received less fertilizer N than corn in other rotation stages ( p  < 0.05) ( Figure 5 ). Similarly, available N supply from manure was partially driven by sod N credits ( R 2 m = 0.13), with significantly lower manure N rates in COS1 compared to other stages of the rotation. Results show that N supply from fertilizer and manure together were reduced by 0.64 units with each one unit increase in sod N credits, mostly due to reductions in manure N applications for first year corn fields after sod.

Table 6 . Summary statistics for different linear mixed models fitted to explore the relationship between different nitrogen (N) sources and rotation stage.

Figure 5 . Area-weighted average available nitrogen (N) from fertilizer and manure applications (colored bars, A ), and marginal means estimated by linear mixed effect model outputs (B) , across all farms and years and for different stages of the crop rotation. In (A) , blue numbers (line one) on top of the graph represent number of observations in each category, and green numbers (line two), the area-weighted average N credits from sod for observations in each rotation stage. Black bolded numbers on top of each bar represent the sum of the area-weighted average available N from fertilizer and manure. In (B) , values for rotation stages that share the same letter within each row (fertilizer N, manure available N, combined manure available N and fertilizer N, available N balance and yield), have estimated marginal means not significantly different at p  = 0.05. COS1, COS2, COS3 = first, second and third crop year of corn silage after sod.

Total N balances per ha, yield-scaled available N balance, and N uptake/available N supply differed among SMGs ( Table 5 ). This was not the case for available N balances per ha. In general, fields in SMGs 3 and 4 showed the lowest balances per ha and per Mg of silage, and highest N uptake/available N supply ratios, whereas fields from SMG 1 showed the highest balances (data not shown). This reflected in part lower yields in SMG 1 ( Figure 2 ) combined with similar or higher total N supply for this group, compared to higher yielding groups. Differences were also observed for available and total balances per ha across soil N uptake efficiency categories, but not for yield-scaled available N balance and N uptake/available N supply. In general, the low soil N uptake efficiency category (≤60%) showed lower balances than other categories, primarily driven by lower available and total N supply. However, despite differences across categories of these variables, SMG, soil N uptake efficiency categories, and drainage class explained a small portion of the variability for most indicators (R 2 m ≤ 1%, Table 5 ).

3.7 Farm-level indicators

Nitrogen supply varied considerably at the farm level ( Figure 6 ). Average contributions from sod N ranged from 5 to 74 kg N ha −1 across farms, and average fertilizer use varied from 6 to 134 kg N ha −1 . Available N supply from manure ranged from 24 to 72 kg N ha −1 for organic N, and from 1 to 57 kg N ha −1 for inorganic N. Average percent inorganic N availability from manure was 38, 30, 49, 36, 1, 40, 45, and 31 for Farms 1–8, respectively. The difference between available and total N supply ranged from 94 kg N ha −1 for Farm 3 to 219 kg N ha −1 for Farm 8.

Figure 6 . Area-weighted average available and total nitrogen (N) supply from different sources (bars) across all observations in individual farms, and area-weighted average for N uptake (horizontal black line). Black bolded numbers on top of the bars correspond to the addition of individual N sources. Numbers in the upper portion of the figure correspond to area-weighted average available and total N balances (1st line, kg N ha −1 ), average annual area-weighted standard deviation for available and total N balances across years of data collection (2nd line, kg N ha −1 ), and area-weighted average yields and coefficient of variation across years of data collection (3rd line, Mg ha −1 , %). Bars with values equal or smaller than 20 kg N ha −1 are not labeled.

Farm-level averages for yield varied from 37.5 Mg ha −1 (Farm 5) to 51.8 Mg ha −1 (Farm 3). The largest variability in yield from year to year was observed for Farms 1, 7, and 8 (coefficient of variation = 13%) while Farm 3 showed the lowest variability (coefficient of variation = 4%).

Averaged at the farm level, available N balances ranged from 46 kg N ha −1 (Farm 3) to 163 (Farm 7) kg N ha −1 . Total N balances ranged from 141 kg N ha −1 (Farm 3) to 379 kg N ha −1 (Farm 7). Mean annual standard deviation for each of the farms for available N balance ranged from 50 kg N ha −1 (Farm 2 and 5) to 86 kg N ha −1 (Farm 6). A lower standard deviation indicates all fields in the farm have similar balances, and a more even allocation of N resources. Average yield-scaled available N balances were 1.8, 2.2, 1.0, 3.2, 2.7, 3.4, 4.4, and 3.9 kg N Mg −1 for Farms 1–8, respectively. Average N uptake/available N supply values were 0.78, 0.71, 0.85, 0.63, 0.69, 0.60, 0.53, and 0.56 for Farms 1–8, respectively.

Animal densities across farms ranged from 1.98 AU ha −1 for Farm 1 to 3.58 AU ha −1 for Farm 8 ( Table 1 ). Linear models testing the relationship between farm animal density, and average available N balances per ha, total N balances per ha, and total manure N supply showed p values ≤0.07 ( Figure 7 ). All these indicators increased with higher farm animal densities. This was not the case for the relationship between farm animal density and average fertilizer N application.

Figure 7 . Relationship between farm animal density, and (A) area-weighted farm averages for available nitrogen (N) balance, (B) total N balance, (C) total manure N supply, and (D) fertilizer N supply. Formulas listed correspond to the linear model explaining the relationship between the two variables plotted. p -values for each predictor tested are listed. Dotted horizontal gray lines represent the area-weighted average for each dependent variable across farms and years. AU, animal unit = 454 kg (1,000 lbs) of live animal weight.

4 Discussion

4.1 nitrogen use indicators varied widely.

Available N balances in the present dataset showed a median of 111 kg N ha −1 . In NY and Vermont (VT) corn silage variety trials, available N balances derived using the same methods as the current study, ranged from −46 to 69 kg N ha −1 across the six locations in 2021, from −26 to 90 kg N ha −1 in 2022, and from −78 to 57 kg N ha −1 in 2023 ( Lawrence et al., 2021 , 2022 , 2023 ). The balances were lower than the median and area-weighted average of the current dataset, mostly reflecting that the average yield in the variety trials was above the median and area-weighted average of our database resulting in lower available N balances (median of 40.6 Mg ha −1 in the present study, versus site*year averages ranging between 44.6 and 80.5 Mg ha −1 in NY and VT corn variety trials). The lower balances and higher yields for the variety trials suggest that for many of the fields in the current database, N may have not been limiting yield.

Sela et al. (2018) reported N balances ranging between 47 to 148 kg N ha −1 for a dataset including 127 on-farm field trials across several US states, where 9% of the observations were corn silage, and 20% included manure applications. Although this study included manure and previous crop N credits, it did not consider soil N contributions. The median available N balance for the current dataset without considering soil N contributions (29 kg N ha −1 ) was lower than the lowest record reported by Sela et al. (2018) .

Median values for N uptake/available N supply and N uptake/total N supply in the present dataset were 0.60 and 0.41, respectively. An evaluation of N uptake/N supply across Wisconsin ranked fields as “low efficiency” when below 0.92, “mid efficiency” from 0.92 to 1.29 and “high efficiency” when larger than 1.29 ( Augarten et al., n.d. ). However, no soil, cover crop or past manure N credits were considered in the N supply estimations, which results in higher efficiencies than the present study, and in some cases larger than one. In comparison, if those N credits were not taken into account, median N uptake/available N supply and N uptake/total N supply would be 0.96 and 0.54, respectively, in the current database.

Although most of N sources were accounted for in the estimations from the present study, 14% of the observations had N uptake/available N supply values above 90, and 8% had negative available N balances. Balances below zero can occur when the crop is more efficient at utilizing available N than what current N supply estimates give it credit for, or when a specific N supply pool is underestimated. Larger quantities of N could be available from (1) manure (higher N content in manure than the one reported in the laboratory analysis, due to variability attributed to sampling and analyses, and/or higher plant N availability than what is currently stipulated by book values), (2) sod N credits (in situations where percentage of legumes in the sod mix was higher than estimated, or environmental conditions favored higher N mineralization from plant biomass than what estimated through book values), (3) soil N supply (fields with long history of manure application, high levels of soil organic matter, and/or favorable conditions for mineralization of soil organic N may result in higher soil N supply than what was defined through book values), and (4) atmospheric N deposition, although contributions should be minor (9 kg N ha −1 ) ( Baumgardner et al., 2002 ).

In general, farms with lower average available N balances across their land base exhibited the largest proportion of high efficiency fields in the current dataset. A total of 40% of fields in Farm 3 had N uptake/available N supply of 90% or higher. This farm was characterized by lower average N supply and higher yields than other farms. Fields with higher N uptake/available N supply were also higher yielding. This is consistent with the current N guidelines for corn silage in NY that recognize lower N needs per Mg of silage produced in higher-yielding fields to account for a potential underestimation of N supply from soil with use of book values in conditions of high productivity ( Ketterings and Workman, 2023 ). Higher yielding observations are then more prone to show lower or negative N balances due to larger N removal, and higher likelihood of underestimation of N supply with use of book values for some sources of N.

Consistent year to year differences explained small portions of the variability in N use indicators compared to farm to farm and field to field differences. Farm was the single most relevant driver across model random effects, and particularly important to explain the variability in available balance per ha, and all indicators involving total N supply (total N balances per ha, yield-scaled total N balances, and N uptake/total N supply). For this last group, field also explained a large portion of the variability. For yield-scaled available N balance, and N uptake/available N supply farm*year explained the largest portion of the variability. These results suggest the indicators effectively point at farm-to-farm differences, and therefore, opportunities for improvement in management. Furthermore, as the different N indicators show slightly different drivers, combining several indicators in a single evaluation can help assess different aspects of farm management (i.e., N supply, field productivity).

Some of the N balance indicators varied across different SMGs and soil N uptake efficiency categories in this study (not drainage classes). Similarly, Sela et al. (2018) found soil texture to be a significant predictor of N surplus when analyzing corn fields in multiple locations across the US. On the contrary, N uptake/N supply documented for corn grain and silage fields in Wisconsin did not significantly differ by soil type ( Augarten et al., n.d. ). Although significant differences existed, the explanatory power of these soil characteristics for the variance across indicators was lower than other factors analyzed. Differences in performance across SMGs, with fields in SMGs 3 and 4 showing lower balances than fields in SMG 1, can be partially attributed to higher yields in SMGs 3 and 4 and in part to management. Fields in SMG1 are generally expected to attain lower yields than other soil management groups, but they had a similar level of N supply to fields in other SMGs resulting in larger N balances (data not shown). This points at the opportunity of reducing N application in SMG 1 fields according to realistically attainable crop yields.

4.2 Corn yield impacted N use indicators less than N supply, but varied by farm and soil characteristics

Farm-to-farm differences explained the largest portion of the variance in yield. Different environmental conditions and management strategies associated with each farm have an influence on yields achieved. The farm*year interaction showed the second largest explanatory power for yield across random effects. This could be explained by (1) variation in management among farms for different years, or (2) inconsistent variation of weather patterns across farms, from year to year that impacted yields differently. Precipitation patterns during the years analyzed varied across Farms and years, particularly for farms 1, 3 and 5, located in the northern and northeastern part of the state, compared to the rest of the dataset ( Supplementary Figure S1 ). Similarly, different patterns existed for yield variations across farms and years. The relevance of the farm*year interaction to explain the variance in yield, likely also explains the relevance of this factor for explaining the variability in yield-scaled available N balance, and N uptake/available N supply, for which N uptake (yield), played a larger role than for balances per ha. Field to field differences explained 16% of the variance in yield, while consistent year-to-year differences did not explain any of the variance in yield. Similarly, Tenorio et al. (2021) reported larger variations in yield and N inputs across fields than across years.

In general, fields from SMGs 2–5, generally associated with higher pre-defined soil N uptake efficiency and better drainage showed overall enhanced yields. This is not surprising as fields with better drainage systems tend to show better yield performance in humid climates such as NY.

Nitrogen supply, and particularly manure N supply had an impact on yield. Manured fields showed higher N supply, which may have ensured no N limitations. Farmers may also choose to apply manure nutrients in higher-yielding fields. Another potential explanation is that manure applications can increase yields beyond its nutrient value, via improved soil biological activity, nutrient cycling, soil pH and increased soil organic carbon ( Cai et al., 2019 ; Ramos Tanchez et al., 2023 ). Neither fertilizer applications nor sod N contributions explained large portions of the variation in yield in the current database, although fields with sod N contributions showed lower productivity (estimated marginal means of 40.8 vs. 42.1 Mg ha −1 , respectively).

4.3 Nitrogen supply considerably affected N use indicators

Nitrogen supply was a larger driver for all N use indicators compared to N uptake. This is consistent with previous studies in Nebraska ( Grassini and Cassman, 2012 ; 60% of the variability explained by N supply), Ohio ( Hanrahan et al., 2019 ; 67% of the variability in agronomic N balances explained by N applications), and across the US ( Sela et al., 2018 ). Tenorio et al. (2021) also documented a larger influence of N inputs than yield in defining field-level N balances for corn fields in Nebraska (88% of the variability in balances was explained by N inputs, and only 12% by yield). These results suggest that producers’ decisions on N inputs influence N use indicators more than yield affected by weather variation across years.

Farm to farm differences explained more of the variation in available N supply (28%) than field to field differences (13%), year to year differences (4%), or the farm*year interaction (10%) (data not shown). A relatively steady manure N supply (animal units in the farm not varying drastically from year to year) and similar management strategies within each operation across years may explain why N supply was highly associated with individual dairies and less impacted by year to year or field to field management differences. Similarly, little variation in N supplied to corn across years was seen in field-level N balances in Nebraska ( Tenorio et al., 2021 ).

Larger balances per unit of land were associated with high N supply and low-yielding fields ( Figure 4 ) which suggests that factors other than N supply limited yield. These could be in-season factors that prevent a field from achieving its yield potential (rainfall, pest pressure), or (semi) permanent limitations in certain environments (soil type, depth to bedrock, compaction, drainage) not acknowledged in N application planning. Consistent with our findings, Tenorio et al. (2021) showed that fields with largest balances were lowest yielding and experienced no additional benefit from large N supply. The same study showed that fields with consistently high balances across years had higher fertilizer N applications (no manured fields were considered), and a larger mismatch between N inputs and economic optimum N rate. Ranking of fields on a farm from lowest to highest available N balances and representing N uptake and N supply for each field will allow for quick identification of fields where N was unlikely to limit yield, and may have been over applied (see an example for Farm 8 in Figure 8 ). Fields on the right-hand side of the graph show high available N balances. Fields with larger unavailable manure inorganic N than available manure inorganic N indicate inefficient use of this N fraction.

Figure 8 . Nitrogen (N) supply in individual corn silage fields from different sources (bars), available N supply (black cross), and N uptake (black dot), in Farm 8 during the 2021 crop year. Fields are arranged according to increasing available N balances from left to right (larger gap between black cross and black dot). Numbers on top of the bars represent the net manure inorganic-N utilization efficiency for individual fields.

4.4 Manure-N and Sod-N nutrient replacement value impacted N use efficiency

Farms showed a wide range for manure and fertilizer N supply, reflecting differences in manure availability and management strategies. Available manure organic and inorganic N played the largest roles in explaining the variability of N use indicators, with available N balances increasing and N uptake/available N supply decreasing with an increase in manure N supply. Furthermore, balances in manured fields were significantly higher than in fields not receiving manure. Fertilizer was also relevant to explain variability in available N balances per ha, yield-scaled available N balances and N supply/available N uptake, but to a smaller degree. These findings are similar to previous research that showed larger N balances in areas with manure N applications, compared with others receiving only inorganic N fertilizer ( Khanal et al., 2014 ; Sela et al., 2018 ; Hanrahan et al., 2019 ). Improving manure N utilization can then help enhance N use efficiency in these farms. Opportunities to reduce large balances in cropping systems where manure nutrients are available may differ from those where only fertilizer nutrients are applied, given the complexities associated with manure distribution, application, and nutrient value assigned by producers. Access to manure storage, availability of application equipment and their operation costs, and number of days suitable for manure application all affect a farmer’s ability to deliver manure nutrients with the right rate, at the right time and application method ( Ribaudo et al., 2011 ). In addition, uncertainty in manure nutrient content and release and the availability of land for manure spreading can contribute to a larger mismatch between crop N requirements and N applied. The 0.2 unit decrease in fertilizer N application in corn fields with a 1 unit increase in available N from manure documented in the present study suggests that manure is valued as an N source but uncertainty about the amount by which fertilizer N could be reduced exists. Considering all these possibilities, identifying farm-specific management strategies (infrastructure, equipment, land availability, nutrient management planning, labor, farmer perception) that can yield the largest return in manure nutrient utilization is necessary to advance N use efficiency.

Large reductions in manure and fertilizer N applications were documented with increases in N availability from sod, mostly driven by lower manure N applications for first year corn silage after sod in a rotation (COS1). This shows that farmers valued sod N credits, consistent with research that showed external N application can be reduced to starter N fertilizer only when corn is grown in rotation with sods ( Lawrence et al., 2008 ; Yost et al., 2014a ). However, despite this reduction in fertilizer and manure N applied to first year corn, the average available N supply remained substantial (71 kg N ha −1 ), which resulted in average available N balances for COS1 higher than for fields with no sod N credits. This could indicate farmers in the current study valued sod N credits less than the value assigned in land-grant university guidelines ( Ketterings and Workman, 2023 ), and/or that producers applied manure beyond crops N needs, to supply nutrients such as P and K depleted under sod years, limiting N use efficiency. Similarly, previous work in Minnesota showed limited adoption of proper rotation (alfalfa) and manure N crediting by growers ( Yost et al., 2014b ). Furthermore, in the present study N applications from manure and fertilizer did not differ significantly between fields in second or third corn crop after sod (COS2, COS3), and those with no sod N credits. This would indicate there is no crediting of sod N contributions beyond the first year after switching from sod to corn silage in the rotation, similar to findings from Yost et al. (2014b) .

The current database showed large farm-to-farm differences between available and total N supply, mostly driven by manure rates used in fields, and the percent of total land base for which spring injection/incorporation was done. Total N balances will always be larger than available N balances due to unavoidable losses of manure N. However, a greater adoption of spring manure injection or incorporation, or in-season manure injection will aid in reducing inorganic N volatilization and can enhance N use efficiency of manure ( Ketterings and Workman, 2023 ). This would then reduce the gap between available and total N balances, as long as farms have the land base to optimally distribute the N. This is particularly relevant for Farms 5, 2, and 8 that showed the lowest average percentage of manure inorganic N availability across all farms (1, 30, and 35%, respectively). Technologies that allow in-season manure application may help increase N use efficiency from manure beyond the best results documented in this study (49% average, Farm 3) ( Sela et al., 2018 , 2019 ).

4.5 Farm animal density was associated with N use indicators

Nitrogen use indicators showed large ranges when averaged at the farm level. This highlights the opportunity of those operations with higher N balances and lower N uptake/N supply to work on optimizing N management. Moreover, within-farm variability of N balances differed. Farms with high average standard deviations for N balances may have opportunities to better distribute their N sources across fields. However, animal density played a significant role and can limit what efficiency levels a farm may reach. Animal densities tended to correlate with greater availability of manure N per unit of corn silage land unless a portion of the manure was used for other crops on the farm. Multiple studies reported in the past the relationship between farm-level N balances, and farm animal density ( Cela et al., 2015 ; Ros et al., 2023 ). Ros et al. (2023) showed, for 47 farms in the northeastern US, a higher likelihood of surpassing feasible whole-farm N balances of 118 kg N ha −1 when animal densities exceeded 1.95 AU ha −1 . Farms 7 and 8, with densities considerably higher than this threshold, showed the highest available and total N supply, as well as total manure N supply. Farms 4, 5 and 6 also had animal densities larger than 1.95 AU ha −1 but their average total manure N contributions in corn silage fields were not considerably larger than Farms 1–3, with lower animal densities. One possible explanation is that these farms had a relatively larger area devoted to crops with large N requirements, other than corn silage ( Table 1 ), providing alternative options for manure allocation, such as sod land with low legume content, winter cereals, or corn grain. This was not the case for Farms 7 and 8, which had a smaller proportion of land that could demand high N applications. This factor, paired with low or no manure exports, would explain why increases in animal density in Farms 7 and 8 were associated with high N balances in corn silage fields.

These results suggest that effectively managing farm animal density, considering farm N needs according to crop rotations (sod N contributions varied largely by farm) and the ability of the land base to recycle manure nutrients, may help improve corn silage and whole-farm N use indicators. The results for the higher animal density farms also show that when manure N supply surpasses farm N needs after accounting for other N sources, manure exports need to be considered.

4.6 Adaptive management should consider field balances for corn silage

State policy in NY allows producers to opt for an Adaptive Management Process and experiment with higher N application rates than recommended by land-grant university guidelines, given field yield is measured and an environmental assessment is conducted to evaluate if the extra N was needed ( Ketterings et al., 2023 ). The results presented suggest field balances for corn silage can be an effective N use efficiency metric in those scenarios, and when conducting general nutrient use efficiency assessments. Balances are sensitive to farm management changes and can point towards opportunities for improvement. However, such an option should include estimation of multiple field N use indicators, and will require the setting of feasible limits.

5 Conclusion

Variability in N use indicators for corn silage fields was primarily driven by N supply. Farm-to-farm differences explained the largest portions of the variability in N supply, yield and N use indicators, and year-to-year changes, the lowest. Manure organic and inorganic N played the largest roles in explaining the variability in N use indicators, with balances increasing and N uptake/N supply decreasing as manure N supply increased. Balances increased with animal density. Properly crediting sod and manure N contributions, increasing manure inorganic N utilization efficiency, reducing animal densities or exporting manure, can aid in improving field N use indicators. Future work is needed to identify feasible ranges for field-level N balances and incentivize the implementation of this assessment through adaptive nutrient management policies.

Data availability statement

The datasets presented in this article are not readily available because they are owned by the farmers participating in the project. Requests to access the datasets should be directed to QK, [email protected] .

Author contributions

AO: Conceptualization, Methodology, Writing – review & editing, Formal analysis, Investigation, Project administration, Writing – original draft. KW: Writing – review & editing. QK: Writing – review & editing, Conceptualization, Funding acquisition, Methodology, Supervision.

The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. This research was funded by a USDA-NIFA grant, funding from the Northern NY Agricultural Development Program (NNYADP), and contributions from the NY Corn Growers Association (NYCGA) managed by the NY Farm Viability Institute (NYFVI). The lead author was funded by a graduate teaching assistantship supplied by the Department of Animal Science, Cornell University.

Acknowledgments

We thank farmers and their certified crop advisors who shared farm data, were involved with data quality checks, and discussed findings with our team. We also extend our gratitude to undergraduate students William Salamone, Joseph Kelly, and Skylar Cooper, and Cornell Nutrient Management Spear Program staff members Manuel Marcaida III and Abraham Hauser, who helped with data collection and processing. We thank Joe Guinness and Erika Mudrak, from the Cornell Statistical Consulting Unit for their help with the statistical analysis. We also appreciate the feedback provided by Kristan Reed and Daryl Van Nydam during the execution of the project.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Supplementary material

The Supplementary material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fsufs.2024.1385745/full#supplementary-material

Abbreviations

AU, animal unit; CAFO, Concentrated Animal Feeding Operations; CNMP, Comprehensive Nutrient Management Plan; COS, corn silage; CSNT, corn stalk nitrate test; SMG, soil management group.

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Keywords: field balance, dairy, New York, nutrient budget, manure conceptualization, formal analysis, investigation, methodology

Citation: Olivo AJ, Workman K and Ketterings QM (2024) Enhancing nitrogen management in corn silage: insights from field-level nutrient use indicators. Front. Sustain. Food Syst . 8:1385745. doi: 10.3389/fsufs.2024.1385745

Received: 13 February 2024; Accepted: 11 July 2024; Published: 09 August 2024.

Reviewed by:

Copyright © 2024 Olivo, Workman and Ketterings. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Quirine M. Ketterings, [email protected]

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

August 2024 Outlook: A La Niña Watch in the dog days of summer

You’ll have to excuse this ENSO Blogger for a minute while I drink some sweet tea on the porch and lament about all the heat and humidity, “Woof, it’s hot!” Things truly seem to move slower during the dog days of summer in late July and early August. And it seems like the El Niño/Southern Oscillation ( ENSO ), a climate pattern in the tropical Pacific Ocean that can affect weather across the world, is no exception. The expected transition from ENSO-Neutral to La Niña continues to proceed slowly, as if the whole Pacific is stuck in a summer daze, moving as slow as molasses.

According to the August ENSO outlook , ENSO-Neutral conditions remain across the Pacific, with La Niña favored to develop during Autumn (66% chance) before persisting through the Northern Hemisphere winter (74% chance during November-January).

Out of the three climate possibilities—La Niña, El Niño, and neutral—forecasts say that neutral conditions are the most likely for the July-September season (tall gray bar above the JAS label, over 80 percent chance). By the September-November (SON) season, La Niña has the highest chance of occurring (blue bar, above 65 percent chance). NOAA Climate Prediction Center image.  

Can I get a fan, please?

All that summer heat can make one forgetful so let’s go back over why we care so much about ENSO. First, a reminder that El Niño and La Niña are opposite phases of ENSO. El Niño occurs when the water in the eastern and central tropical Pacific is warmer than average, while La Niña occurs when the water is cooler than average. That change in water temperature can jumble up the tropical atmosphere above it, causing a global cascade of atmospheric impacts. The end result is shifts in the jet stream and changes in weather patterns, often leading to wild weather including floods, droughts, and heatwaves.

But unlike other climate phenomena which can be difficult to predict even two weeks prior, the phase of ENSO can be predicted many months in advance, giving communities worldwide vital time to prepare.

ENSO-Neutral, meanwhile, simply reflects that ocean temperatures are near-average, with little unusual impacts on the atmosphere above it.

Toes in the water

The last month has seen near-average sea surface temperatures across most of the equatorial Pacific Ocean, so we are squarely ENSO-Neutral. The Niño-3.4 region of the Pacific, which is our primary monitoring region for ENSO , was 0.1°C warmer than the long-term average from 1991-2020, according to ERSSTv5, our most reliable sea surface temperature dataset. Yep, I did say “warmer.” So let’s dive deeper into the Pacific for the reason we are still forecasting the development of La Niña (eventually) this year.

While sea surface temperatures are currently perfectly cromulent , below-average water temperatures in the subsurface (the surface to 300 meters below the surface) of the tropical Pacific Ocean strengthened in the past month, expanding across more of the central and eastern Pacific Ocean. These cooler-than-average waters will be one of the driving forces behind any La Niña that forms later this year.

Beneath the surface of the tropical Pacific Ocean at the equator, a deep pool of cooler-than-average (blue) waters has been building up throughout the summer to date (June 7-August 1, 2024). This pool of relatively cool water is a key factor behind the prediction for La Niña later this fall and winter. NOAA Climate.gov image, based on analysis from Michelle L'Heureux, Climate Prediction Center. 

Meanwhile, for as interesting as the ocean has been, the atmosphere has been the opposite. The trade winds were slightly more easterly than normal in July (which reflects slightly stronger-than-average trade winds), while thunderstorm activity was generally near-average. A pretty clear reflection of ENSO-Neutral.

Whew! When is that cold front coming?

Let’s talk about the forecast. So far this summer, the climate models we use for guidance have been trending toward a weaker and delayed development of La Niña than they first hinted in the spring. In May and June, the models forecasted a start during summer. But the most recent forecasts pinpoint early fall as the most likely start time. Regardless, the overall model consensus remains that La Niña will likely form this year and last through the upcoming winter.

Line graph showing observed and predicted temperatures (black line) in the key ENSO-monitoring region of the tropical Pacific from winter 2023-24 though winter 2024-25. The gray shading shows the range temperatures predicted by individual models that are part of the North American Multi Model Ensemble (NMME, for short). Most of the shading appears below the dashed blue line by the fall, meaning most models predict that temperature in the Niño-3.4 region of the tropical Pacific will be cooler than average by at least 0.5 degrees Celsius (0.9 degrees Fahrenheit)—the La Niña threshold. NOAA Climate.gov image, based on data provided by Climate Prediction Center. 

This fact, combined with current observations of cooler-than-average water at depth across the Pacific and slightly enhanced trade winds, give forecasters confidence that even though the transition to La Niña has been slower than initially expected, it’s still likely to form later this year.

Can someone just turn the AC on?

The burning-hot, must-be-suffering-from-heat-exhaustion-at-this-point elephant in the room continues to be the hot streak Earth has been going through for more than a year (as well as the longer warming trend associated with human-caused climate change). Global ocean temperatures have been record breaking every month for well over a year. And just as we noted some uncertainty on how this would affect the 2023-24 El Niño (RIP), the same goes for any developing La Niña.

Daily surface temperatures for the global oceans between 60˚ North and South from 1981-2024 to date. From April of 2023 (orange line) through May 2024 (red line) temperatures were not only been much warmer than the 1981-2010 average (thick blue-green line), but record warm by a wide margin compared to all other years (thin blue-green lines). NOAA Climate.gov image adapted from Climate Reanalyzer . Explore NOAA's official monthly rankings with the Climate at a Glance tool from the National Centers for Environmental Information.

This overall heat brings me back to an excellent post Michelle wrote a few years ago describing the relative Oceanic Niño Index, which takes the temperature anomaly in the Niño3.4 region and subtracts the anomaly for the entire tropics. This helps to shine a clearer light on the local regions of relative warming or cooling across the Pacific, which is directly tied to increased or decreased tropical rainfall. While it is not NOAA’s official ENSO index , it is a newer index that the team monitors on the side. 

Said more simply, it’s not just how much warmer or colder than average that part of the tropical Pacific is that jumbles up the tropical atmosphere, it’s the difference in warming or cooling compared the rest of the Pacific. And that’s where the relative ONI comes in!

So where do things stand? The regular ONI for May-July was +0.2°C. But the relative ONI was already down to -0.4°C. That means there could be a scenario later this year where the ONI is not yet below -0.5°C, but the relative ONI already is, and the atmosphere might start reflecting La Niña-like impacts. Doesn’t that seem like a pain to communicate? Tell me about it!

Luckily for me, if that does happen it will be Emily’s job to communicate it all when she’s back writing the monthly outlook posts next month. Sorry, Emily.

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Related content, news & features, december 2020 la niña update: walking in a la niña winter wonderland, september 2021 enso update: feeling groovy, time lapse of ocean temperatures shows el niño fading, hints of la niña, maps & data, sst - enso region, monthly difference from average, precipitation - monthly percent of average, precipitation - 1991-2020 monthly average, teaching climate, climate youth engagement, toolbox for teaching climate & energy, international climate change forum, climate resilience toolkit, sea surface temperature anomaly—december 2010, preparing for la niña, seasonal climate forecast serves as a call to action.

Illinois Crop Update – August 9, 2024

Department of Crop Sciences University of Illinois

Russ Higgins – Commercial Agriculture Educator

Grundy County

Soil Conditions: Mildly Dry (soil is drier than normal, plant growth may have slowed)

This week precipitation has been mixed in northeast Illinois, areas north of the Rte. 80 corridor have received multiple rainfall events while areas south have received limited to no precipitation. Soy continues to progress, reaching R5, beginning seed stage. Depending upon variety maturity and planting date, most corn fields visited were R3-R4 to R4-R5. An unwelcome discovery this week was significant Tar spot symptoms in several corn fields. The decision to use a fungicide treatment is difficult in later maturity stages, especially when we are near R5, the dent stage. A reminder that grain fill in the dent stage often extends to 30 days or more. However, with declining commodity prices farmers need to consider their ROI (return on investment) when considering a or additional fungicide applications. A tool available to aid in the decision making is the Corn Fungicide ROI Calculator from the Crop Protection Network. The purpose of the Corn Fungicide ROI Calculator is to share results from university uniform corn fungicide trials conducted in the United States and Canada and allow farmers and others in the agricultural industry to calculate the potential return on investment (ROI) for corn fungicide application across a variety of user-defined factors, which is based on research data included in this calculator. The two variables needed are expected corn yield and marketing price. The calculator can be accessed here .

Figure 1: Tar spot; Grundy County – Aug 6th, 2024

Figure 2: R5 Beginning seed soy – Aug 6th, 2024

Reagan Tibbs  – Commercial Agriculture Educator

Logan County

Soil Conditions: Near Normal

Crops across Logan, Menard, and Sangamon counties continue to grow and develop nicely, thanks to last week’s rain showers. Despite having high winds during some storms, there does not appear to be any wind damage to the crops. Many soybean fields are in the R4 stage, with some earlier planted fields beginning to develop seeds (R5). Much of the same can be said for the corn crop as well. Most fields are in the R4 dough stage, with some of the earlier planted fields beginning to dent (R5). The alfalfa across the area has grown back nicely as well, with last week’s rains and this week’s cooler temperatures helping. Most fields look to be in Stage 3 (early bud).

Talon Becker  – Commercial Agriculture Specialist

McLean County

This past week has brought some drier weather to east central Illinois than the week prior.  In the eastern half of McLean County, soil moisture conditions looked good, overall, during my survey.  Most fields have started to dry in the top couple of inches of the soil profile, but some parts of low-lying and/or poorly drained fields still had soils that were at field capacity.  Compared to some of my surveys in recent weeks in neighboring counties, I saw very few soybean fields showing spots of plant stress due to waterlogging and/or soil-borne pathogens.  A few of the corn fields I visited were showing early signs of stalk rot in some plants, although at this time, this seemed to be largely limited to those wetter fields I mentioned earlier.  Something else I saw in several of these wetter corn fields was corn ears containing both brown, senesced silks and unpollinated silks.  The tassels in these fields did not appear to be shedding any viable pollen, which may mean it’s too late for these silks/ovules to make grain.  Most corn was in the late R3 (milk) to early R4 (dough) range, but I did find one field at early dent (R5) and another still at blister (R2).  Soybeans ranged from R3 (beginning pod) to late R5 (beginning seed), with most falling in the latter half of that range.

Figure 3: Pod from soybean plant approaching full seed (R6)

Figure 4: Corn ear with unfertilized ovules at the ear tip

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Field collaborative recognition method and experiment for thermal infrared imaging of damaged potatoes

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Bibliometrics & citations, view options, recommendations, design of pcr temperature control system based on infrared thermal imaging.

In the electrochemical real-time quantitative PCR instrument, in order to control the humidity of the reagent in the three temperature zones of the thermal cycle, improve the efficiency of the PCR reaction, and solve the delay problem when transferring ...

Land cover characterization for hydrological modelling using thermal infrared emissivities

Remote sensing with multispectral thermal infrared can improve regional estimation of evapotranspiration (ET) by providing new constraints on land surface energy balance. Current models use visible and near infrared bands to obtain vegetative cover, and ...

Thermal infrared inverse model for component temperatures of mixed pixels

Multiangular remote sensing data can be used to retrieve land surface component temperatures, which will have a broad application in the future. For higher resolution pixels of satellite radiometers, the component temperatures may be separated ...

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• Damaged potatoes
• Thermal excitation
• Thermal infrared imaging
• Disability identification
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