Syncronizing plant available water with soil nitrogen (N) remains a critical aspect of agronomic management to enhance crop yield, grain quality, farmers’ profit, and environmental sustainability. ...Their interaction is essential expecially in landscapes characterized by a highly spatial and temporal range of pedoclimatic conditions. To support farmers in making more informed decisions, validate dynamic process based crop simulation models have been successfully used to optimize N fertlization. In this study, we aimed to develop a tactical N fertilizer management strategy to increase profitability, improve grain quality and reduce N losses. The SALUS model was tested against measured durum wheat grain yield and grain quality data collected across independent farmers’ fields in Tuscany (Italy). The model was then used to optimize N fertilization under different potential plant extractable soil water (PESW) conditions at the second topdressing N fertilization timing. The model was tested against measurements of grain yield and protein concentration at harvest, as well as phenological stage, biomass, and plant N content during the growing season. Simulations were carried out for 30 years of available weather, using different N rates. The simulations allowed the identification of optimal N rates for each PESW condition and soil type concerning economic and environmental sustainability. Results showed higher yield and higher leaching for the silty clay soil (Quercia; QUE) than for the loamy soil (Arbia; ARB). No major differences were predicted for protein content across soils. Profitability and emissions increased as N rate increased. The N fertilization strategy locally adopted by farmers was also analyzed across different PESW conditions at 2nd topdressing fertilization in comparison to other adopted N management strategies (timing of application and fertilization rates). The model showed that the conventional fertilization strategy does not maximize socio-ecological benefits, but was also the lowest among all the strategies tested. The maximum economic benefit for farmers was reached by applying 90 kg N at 1st and 60 kg N at 2nd topdressing fertilization, both in dry (due to sufficient soil water storage in the soil) and wet years. These results provide valuable insights for developing strategies that balance sustainability, resilience, quality, and profitability in wheat crop production in central Italy.
•Use of SALUS model to optimize nitrogen (N) fertilization.•Simulation of different potential plant extractable soil water (PESW) conditions.•Simulations were carried out for 30 years, using different N rates.•Identification of N fertilization strategy that maximizes economic returns.
The Midwest is one of the most productive agricultural regions, but mitigating loss of nitrogen (N) from cropland is needed to improve environmental quality. Tradeoffs between crop yield and N loss ...have been linked largely to the inefficient use of N fertilizers, but the contributions of more systemic factors such as soil characteristics, crop sequences, and genotypes have not been thoroughly studied. This dissertation examines and quantifies the impact of various genetic, environmental and management drivers of crop yield and N-loss tradeoffs in the maize and soybean cropping systems of the US Midwest, and identifies potential management strategies to lessen these tradeoffs. To this end, a system analysis framework was employed, which used field data from small plots, long-term experiments, publicly available databases, and process-based modeling. The approach allowed for full exploration of the soil-plant-atmosphere continuum and extrapolation of the behavior of cropping systems across a wide range of weather, soil, and management. Findings from these studies indicate the prominent role of crop sequences and residue dynamics in driving tradeoffs. In maize-soybean systems, it was estimated that a majority (55%) of N losses originated from the release of native soil N into the environment due to asynchrony between soil mineralization and crop uptake. Including a rye cover crop in rotations was shown to be an effective way of improving soil N retention and reducing losses, while seldom resulting in yield tradeoffs. However, the most effective strategies also required simultaneously choosing appropriate genotypes, timely planting, and optimizing N inputs to better match crop requirements. Research also aimed to advance knowledge and modeling of various crop-soil processes including: maize, soybean and rye growth; water and N cycling; and a novel algorithm to simulate grain dry down of maize and soybean.
Winter canola (Brassica napus) could be a good candidate for enhancing cropping systems in Iowa because of its potential to provide environmental benefits and produce a marketable crop compatible ...with existing grain production and distribution schemes. However, it is still uncertain whether this crop would be suitable for helping balance environmental and financial goals of conventional cropping systems under the environmental and market conditions unique to Iowa. The work presented in this thesis is an effort to assess the suitability of winter canola for providing environmental benefits while fitting within the logistic and economic constrains of current cropping systems. Based on observations from experimentation in field plots, it is determined that canola can be successfully established in the fall, survive the winter, and regrow in the spring, but adequate conditions during fall growth are crucial. It is estimated that seeding by 31 Aug in the north to 12 Sep in the southeast will allow enough time for adequate growth of canola during the fall in at least half of the years in Iowa. Because these seeding date requirements will likely conflict with standing crops during most years, adjustments to the rotation schemes of conventional rotations are needed. Therefore, two alternative systems are proposed, and their economic profiles are studied. Findings from this economic analysis suggest that these rotation alternatives produce relatively less net returns than the conventional corn (Zea mays L.)- soybean (Glycine max (L) Merr.) rotation, throughout a range of market and canola yield scenarios. Based on these results, it is determined that although winter canola can provide some environmental and economic enhancements to summer annual crop rotations in Iowa, but the specific situations in which canola can fit these rotations are limited. Nonetheless, more research is needed to fully understand the productivity potential of winter canola in Iowa, before counting these as feasible alternatives for producers in this state.
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.
The increased spring rainfall intensity and amounts observed recently in the US Midwest poses additional risk of nitrate (NO3) leaching from cropland, and contamination of surface and subsurface ...freshwater bodies. Several individual strategies can reduce NO3 loading to freshwater ecosystems (i.e. optimize N fertilizer applications, planting cover crops, retention of active cycling N), but the potential for synergistic interactions among N management practices has not been fully examined. We applied portfolio effect (PE) theory, a concept originally developed for financial asset management, to test whether implementing multiple N management practices simultaneously produces more stable NO3 leaching mitigation outcomes than what would be predicted from implementing each practice independently. We analyzed simulated data generated using a validated process-based cropping system model (APSIM) that covers a range of soils, weather conditions, and management practices. Results indicated that individual management practices alone explained little of the variation in drainage NO3 loads but were more influential in the amount of residual soil NO3 at crop harvest. Despite this, we observed a general stabilizing effect from adopting well-designed multi-strategy approaches for both NO3 loads and soil NO3 at harvest, which became more pronounced in years with high spring rainfall. We use the PE principle to design multi-strategy management to reduce and stabilize NO3 leaching, which resulted in 9.6% greater yields, 15% less NO3 load, and 61% less soil NO3 at harvest than the baseline typical management. Our results make the case for applying the PE to adapt NO3 leaching mitigation to increased climate variability and change, and guide policy action and on-the-ground implementation.
We used the Agricultural Production Systems sIMulator (APSIM) to predict and explain maize and soybean yields, phenology, and soil water and nitrogen (N) dynamics during the growing season in Iowa, ...USA. Historical, current and forecasted weather data were used to drive simulations, which were released in public four weeks after planting. In this paper, we (1) describe the methodology used to perform forecasts; (2) evaluate model prediction accuracy against data collected from 10 locations over four years; and (3) identify inputs that are key in forecasting yields and soil N dynamics. We found that the predicted median yield at planting was a very good indicator of end‐of‐season yields (relative root mean square error RRMSE of ∼20%). For reference, the prediction at maturity, when all the weather was known, had a RRMSE of 14%. The good prediction at planting time was explained by the existence of shallow water tables, which decreased model sensitivity to unknown summer precipitation by 50–64%. Model initial conditions and management information accounted for one‐fourth of the variation in maize yield. End of season model evaluations indicated that the model simulated well crop phenology (R2 = 0.88), root depth (R2 = 0.83), biomass production (R2 = 0.93), grain yield (R2 = 0.90), plant N uptake (R2 = 0.87), soil moisture (R2 = 0.42), soil temperature (R2 = 0.93), soil nitrate (R2 = 0.77), and water table depth (R2 = 0.41). We concluded that model set‐up by the user (e.g. inclusion of water table), initial conditions, and early season measurements are very important for accurate predictions of soil water, N and crop yields in this environment.
Pre-season prediction of crop production outcomes such as grain yields and N losses can provide insights to stakeholders when making decisions. Simulation models can assist in scenario planning, but ...their use is limited because of data requirements and long run times. Thus, there is a need for more computationally expedient approaches to scale up predictions. We evaluated the potential of five machine learning (ML) algorithms as meta-models for a cropping systems simulator (APSIM) to inform future decision-support tool development. We asked: 1) How well do ML meta-models predict maize yield and N losses using pre-season information? 2) How many data are needed to train ML algorithms to achieve acceptable predictions?; 3) Which input data variables are most important for accurate prediction?; and 4) Do ensembles of ML meta-models improve prediction? The simulated dataset included more than 3 million genotype, environment and management scenarios. Random forests most accurately predicted maize yield and N loss at planting time, with a RRMSE of 14% and 55%, respectively. ML meta-models reasonably reproduced simulated maize yields but not N loss. They also differed in their sensitivities to the size of the training dataset. Across all ML models, yield prediction error decreased by 10-40% as the training dataset increased from 0.5 to 1.8 million data points, whereas N loss prediction error showed no consistent pattern. ML models also differed in their sensitivities to input variables. Averaged across all ML models, weather conditions, soil properties, management information and initial conditions were roughly equally important when predicting yields. Modest prediction improvements resulted from ML ensembles. These results can help accelerate progress in coupling simulation models and ML toward developing dynamic decision support tools for pre-season management.
In addition to soil health and conservation benefits, cover crops (CCs) may offer weed control in the midwestern United States, but individual studies report varying effects. We conducted a ...meta‐analysis of studies measuring weed biomass (WBIO) or density (WDEN) in paired CC and no‐cover treatments in corn (Zea mays L.)–soybean Glycine max (L.) Merr rotations in the U.S. Midwest. Fifteen studies provided 123 paired comparisons of WBIO and 119 of WDEN. Only grass CCs significantly reduced WBIO, while no CC reduced WDEN. We found no evidence CC management factors (e.g., termination method) directly affected outcomes. Our dataset showed that a 75% reduction in WBIO requires at least 5 Mg ha−1 of CC. Simulations from a process‐based model (SALUS) indicated achieving 5 Mg ha−1 requires substantially earlier fall planting and later spring termination in most years, conflicting with typical cash‐crop planting and harvesting. We conclude CCs significantly reduce WBIO, but current CC management constraints render these reductions variable and uncertain.
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