Climate change is likely to increase the frequency of drought and more extreme precipitation events. The objectives of this study were i) to assess the impact of extended drought followed by heavy ...precipitation events on yield and soil organic carbon (SOC) under historical and future climate, and ii) to evaluate the effectiveness of climate adaptation strategies (no-tillage and new cultivars) in mitigating impacts of increased frequencies of extreme events and warming. We used the validated SALUS crop model to simulate long-term maize and wheat yield and SOC changes of maize-soybean-wheat rotation cropping systems in the northern Midwest USA under conventional tillage and no-till for three climate change scenarios (one historical and two projected climates under the Representative Concentration Path (RCP) 4.5 and RCP6) and two precipitation changes (extreme precipitation occurring early or late season). Extended drought events caused additional yield reduction when they occurred later in the season (10-22% for maize and 5-13% for wheat) rather than in early season (5-17% for maize and 2-18% for wheat). We found maize grain yield declined under the projected climates, whereas wheat grain yield increased. No-tillage is able to reduce yield loss compared to conventional tillage and increased SOC levels (1.4-2.0 t/ha under the three climates), but could not reverse the adverse impact of climate change, unless early and new improved maize cultivars are introduced to increase yield and SOC under climate change. This study demonstrated the need to consider extreme weather events, particularly drought and extreme precipitation events, in climate impact assessment on crop yield and adaptation through no-tillage and new genetics reduces yield losses.
The potent greenhouse gas nitrous oxide (N2O) is accumulating in the atmosphere at unprecedented rates largely due to agricultural intensification, and cultivated soils contribute ∼60% of the ...agricultural flux. Empirical models of N2O fluxes for intensively managed cropping systems are confounded by highly variable fluxes and limited geographic coverage; process-based biogeochemical models are rarely able to predict daily to monthly emissions with >20% accuracy even with site-specific calibration. Here we show the promise for machine learning (ML) to significantly improve field-level flux predictions, especially when coupled with a cropping systems model to simulate unmeasured soil parameters. We used sub-daily N2O flux data from six years of automated flux chambers installed in a continuous corn rotation at a site in the upper US Midwest (∼3000 sub-daily flux observations), supplemented with weekly to biweekly manual chamber measurements (∼1100 daily fluxes), to train an ML model that explained 65%-89% of daily flux variance with very few input variables-soil moisture, days after fertilization, soil texture, air temperature, soil carbon, precipitation, and nitrogen (N) fertilizer rate. When applied to a long-term test site not used to train the model, the model explained 38% of the variation observed in weekly to biweekly manual chamber measurements from corn, and 51% upon coupling the ML model with a cropping systems model that predicted daily soil N availability. This represents a two to three times improvement over conventional process-based models and with substantially fewer input requirements. This coupled approach offers promise for better predictions of agricultural N2O emissions and thus more precise global models and more effective agricultural mitigation interventions.
Agricultural systems science generates knowledge that allows researchers to consider complex problems or take informed agricultural decisions. The rich history of this science exemplifies the ...diversity of systems and scales over which they operate and have been studied. Modeling, an essential tool in agricultural systems science, has been accomplished by scientists from a wide range of disciplines, who have contributed concepts and tools over more than six decades. As agricultural scientists now consider the “next generation” models, data, and knowledge products needed to meet the increasingly complex systems problems faced by society, it is important to take stock of this history and its lessons to ensure that we avoid re-invention and strive to consider all dimensions of associated challenges. To this end, we summarize here the history of agricultural systems modeling and identify lessons learned that can help guide the design and development of next generation of agricultural system tools and methods. A number of past events combined with overall technological progress in other fields have strongly contributed to the evolution of agricultural system modeling, including development of process-based bio-physical models of crops and livestock, statistical models based on historical observations, and economic optimization and simulation models at household and regional to global scales. Characteristics of agricultural systems models have varied widely depending on the systems involved, their scales, and the wide range of purposes that motivated their development and use by researchers in different disciplines. Recent trends in broader collaboration across institutions, across disciplines, and between the public and private sectors suggest that the stage is set for the major advances in agricultural systems science that are needed for the next generation of models, databases, knowledge products and decision support systems. The lessons from history should be considered to help avoid roadblocks and pitfalls as the community develops this next generation of agricultural systems models.
•Advances were fastest after events that caused economic or environmental concerns•Technological advances have had major benefits on agricultural system modeling•Progress toward robust models has been enabled through open, harmonized data•Modularity and interoperability are features needed for next generation models•More integration among disciplines and data are needed to advance agricultural models
Systematic monitoring of pasture quantity and quality is important to match the herd forage demand (pasture removal by grazing or harvest) to the supply of forage with adequate nutritive value. The ...aim of this research was to monitor, assess and manage changes in pasture growth, morphology and digestibility by integrating information from an Unmanned Aerial Vehicle (UAV) and two process-based models. The first model, Systems Approach to Land Use Sustainability (SALUS), is a process-based crop growth model used to predict pasture regrowth based on soil, climate, and management data. The second model, Morphogenetic and Digestibility of Pasture (MDP), uses paddock-scale values of herbage mass as input to predict leaf morphogenesis and forage nutritive value. Two field experiments were carried out on tall fescue- and ryegrass-based pastures under rotational grazing with lactating dairy cattle. The first experiment was conducted at plot scale and was used to calibrate the UAV and to test models. The second experiment was conducted at field scale and was used to test the UAV's ability to predict pasture biomass under grazing rotation. The Normalized Difference Vegetation Index (NDVI) calculated from the UAV's multispectral reflectance (n = 72) was strongly correlated (p < 0.001) to plot measurements of pasture biomass (R2 = 0.80) within the range of ~226 and 4208 kg DM ha-1. Moreover, there was no difference (root mean square error, RMSE < 500 kg DM ha-1) between biomass estimations by the UAV (1971±350 kg ha-1) and two conventional methods used as control, the C-Dax proximal sensor (2073±636 kg ha-1) and ruler (2017±530 kg ha-1). The UAV approach was capable of mapping at high resolution (6 cm) the spatial variability of pasture (16 ha). The integrated UAV-modeling approach properly predicted spatial and temporal changes in pasture biomass (RMSE = 509 kg DM ha-1, CCC = 0.94), leaf length (RMSE = 6.2 cm, CCC = 0.62), leaf stage (RMSE = 0.7 leaves, CCC = 0.65), neutral detergent fiber (RMSE = 3%, CCC = 0.71), digestibility of neutral detergent fiber (RMSE = 8%, CCC = 0.92) and digestibility of dry matter (RMSE = 5%, CCC = 0.93) with reasonable precision and accuracy. These findings therefore suggest potential for the present UAV-modeling approach for use as decision support tool to allocate animals based on spatially and temporally explicit predictions of pasture biomass and nutritive value.
Plants remove carbon dioxide from the atmosphere through photosynthesis. Because agriculture's productivity is based on this process, a combination of technologies to reduce emissions and enhance ...soil carbon storage can allow this sector to achieve net negative emissions while maintaining high productivity. Unfortunately, current row-crop agricultural practice generates about 5% of greenhouse gas emissions in the United States and European Union. To reduce these emissions, significant effort has been focused on changing farm management practices to maximize soil carbon. In contrast, the potential to reduce emissions has largely been neglected. Through a combination of innovations in digital agriculture, crop and microbial genetics, and electrification, we estimate that a 71% (1,744 kg CO
e/ha) reduction in greenhouse gas emissions from row crop agriculture is possible within the next 15 y. Importantly, emission reduction can lower the barrier to broad adoption by proceeding through multiple stages with meaningful improvements that gradually facilitate the transition to net negative practices. Emerging voluntary and regulatory ecosystems services markets will incentivize progress along this transition pathway and guide public and private investments toward technology development. In the difficult quest for net negative emissions, all tools, including emission reduction and soil carbon storage, must be developed to allow agriculture to maintain its critical societal function of provisioning society while, at the same time, generating environmental benefits.
Warming generally leads to increased evaporative demand, altering the amount of water needed for growing crops. For the Midwest, some studies have suggested that reaching yield targets by 2050 will ...not be possible without additional precipitation or large expansion of irrigation. Here, we show that this claim is not supported by the historical summer climate trends, which indicate that the warming of daily average temperatures is largely driven by increases in minimum temperatures, while maximum temperatures have decreased. This has translated into a net decrease in vapor pressure deficit (VPD) and potential evapotranspiration (PET). With the increasing rainfall, this suggests that crop water deficits have likely become less frequent in the region despite the warming climate. By projecting these trends into 2050 and ancillary use of a crop model, we estimate minor changes in PET that would have minimal effects on corn yields (<6%) under persistence of these trends.
Agricultural soils can act as a sink for large quantities of soil organic carbon (SOC) but can also be sources of carbon to the atmosphere. The international standard for assessing SOC stock and ...measuring stock change stipulates fixed depth sampling to at least 30 cm. The tendency of bulk density (BD) to decrease with decreasing disturbance and increasing SOC concentration and the assumption of constant SOC and BD within this depth profile promotes error in the estimates of SOC stock. A hypothetical but realistic change in BD from 1.5 to 1.1 g cm
from successive fixed depth sampling to 30 cm underestimates SOC stock change by 17%. Significant effort has been made to evaluate and reduce this fixed depth error by using the equivalent soil mass (ESM) approach, but with limited adoption. We evaluate the error in SOC stock assessment and change generated from fixed depth measurements over time relative to the ESM approach and propose a correction that can be readily adopted under current sampling and analytical methods. Our approach provides a more accurate estimate of SOC stock accumulation or loss that will help incentivize management practice changes that reduce the environmental impacts of agriculture and further legitimize the accounting practices used by the emerging carbon market and organizations that have pledged to reduce their supply chain greenhouse gas (GHG) footprints.
Core Ideas
Higher maize yields do not require more water.
The transpiration efficiency approach results in biased estimates of ET in high‐yielding maize production.
Evapotranspiration does not change ...in higher plant populations if water supply is adequate.
Projected changes in vapor pressure deficit will not lead to greater water use in maize.
This paper describes the results of an analysis demonstrating that high yields in maize can be obtained without additional water under current and projected vapor pressure deficits. The objective of the study was to quantify evapotranspiration (ET) in high‐yielding maize under current and projected vapor pressure deficits using the energy balance contrasted with the transpiration efficiency (TE) approach. This study indicates a lack of accuracy and bias in the TE approach when future crop water requirements were estimated. High maize yields are achievable using on average 700 mm of water as demonstrated by the current record maize grain yield of 34 Mg ha−1, which is ∼23 Mg ha−1 higher than the US average. These yields are achievable with approximately the same ET even under projected changes in vapor pressure deficit, through improved genetics and optimum agronomic management.
The United States Great Lakes Region (USGLR) is a critical geographic area for future bioenergy production. Switchgrass (Panicum virgatum) is widely considered a carbon (C)‐neutral or C‐negative ...bioenergy production system, but projected increases in air temperature and precipitation due to climate change might substantially alter soil organic C (SOC) dynamics and storage in soils. This study examined long‐term SOC changes in switchgrass grown on marginal land in the USGLR under current and projected climate, predicted using a process‐based model (Systems Approach to Land‐Use Sustainability) extensively calibrated with a wealth of plant and soil measurements at nine experimental sites. Simulations indicate that these soils are likely a net C sink under switchgrass (average gain 0.87 Mg C ha−1 year−1), although substantial variation in the rate of SOC accumulation was predicted (range: 0.2–1.3 Mg C ha−1 year−1). Principal component analysis revealed that the predicted intersite variability in SOC sequestration was related in part to differences in climatic characteristics, and to a lesser extent, to heterogeneous soils. Although climate change impacts on switchgrass plant growth were predicted to be small (4%–6% decrease on average), the increased soil respiration was predicted to partially negate SOC accumulations down to 70% below historical rates in the most extreme scenarios. Increasing N fertilizer rate and decreasing harvest intensity both had modest SOC sequestration benefits under projected climate, whereas introducing genotypes better adapted to the longer growing seasons was a much more effective strategy. Best‐performing adaptation scenarios were able to offset >60% of the climate change impacts, leading to SOC sequestration 0.7 Mg C ha−1 year−1 under projected climate. On average, this was 0.3 Mg C ha−1 year−1 more C sequestered than the no adaptation baseline. These findings provide crucial knowledge needed to guide policy and operational management for maximizing SOC sequestration of future bioenergy production on marginal lands in the USGLR.
Switchgrass bioenergy production is known for its ability to increase soil organic carbon (SOC) storage. We examine how projected increases in air temperature and precipitation might alter SOC dynamics under switchgrass grown on marginal land in the United States Great Lakes Region, using a calibrated process‐based model. We found that although soils in this region are likely net carbon sinks under switchgrass, climate change might have a profound impact on SOC storage, reducing gains down to 70% below historical rates in the most extreme scenarios. We also show how adapting management and genotypes might serve to partially offset these impacts.
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