Sotirios V. Archontoulis

How efficiently do corn‐ and soybean‐based cropping systems use water? A systems modeling analysis

Agricultural systems are being challenged to decrease water use and increase production while climate becomes more variable and the worlds population grows. Low water use efficiency is traditionally characterized by high water use relative to low grain production and usually occurs under dry conditions. However, when a cropping system fails to take advantage of available water during wet conditions, this is also an inefficiency and is often detrimental to the environment. Here, we provide a systems-level definition of water use efficiency (sWUE) that addresses both production and environmental quality goals through incorporating all major system water losses (evapotranspiration, drainage, and runoff). We extensively calibrated and tested the Agricultural Production Systems sIMulator (APSIM) using 6 years of continuous crop and soil measurements in corn- and soybean-based cropping systems in central Iowa, USA. We then used the model to determine water use, loss, and grain production in each system and calculated sWUE in years that experienced drought, flood, or historically average precipitation. Systems water use efficiency was found to be greatest during years with average precipitation. Simulation analysis using 28 years of historical precipitation data, plus the same dataset with ± 15% variation in daily precipitation, showed that in this region, 430 mm of seasonal (planting to harvesting) rainfall resulted in the optimum sWUE for corn, and 317 mm for soybean. Above these precipitation levels, the corn and soybean yields did not increase further, but the water loss from the system via runoff and drainage increased substantially, leading to a high likelihood of soil, nutrient, and pesticide movement from the field to waterways. As the Midwestern United States is predicted to experience more frequent drought and flood, inefficiency of cropping systems water use will also increase. This work provides a framework to concurrently evaluate production and environmental performance of cropping systems.

A model for mechanistic and system assessments of biochar effects on soils and crops and trade‐offs

We developed a biochar model within the Agricultural Production Systems sIMulator (APSIM) software that integrates biochar knowledge and enables simulation of biochar effects within cropping systems. The model has algorithms that mechanistically connect biochar to soil organic carbon (SOC), soil water, bulk density (BD), pH, cation exchange capacity, and organic and mineral nitrogen. Soil moisture (SW)–temperature–nitrogen limitations on the rate of biochar decomposition were included as well as biochar‐induced priming effect on SOC mineralization. The model has 10 parameters that capture the diversity of biochar types, 15 parameters that address biochar‐soil interactions and 4 constants. The range of values and their sensitivity is reported. The biochar model was connected to APSIMs maize and wheat crop models to investigate long‐term (30 years) biochar effects on US maize and Australia wheat in various soils. Results from this sensitivity analysis showed that the effect of biochar was the largest in a sandy soil (Australian wheat) and the smallest in clay loam soil (US maize). On average across cropping systems and soils the order of sensitivity and the magnitude of the response of biochar to various soil‐plant processes was (from high to low): SOC (11% to 86%) > N2O emissions (−10% to 43%43%) > plant available water content (0.6% to 12.9%) > BD (−6.5% to −1.7%) > pH (−0.8% to 6.3%) > net N mineralization (−19% to 10%) > CO2 emissions (−2.0% to 4.3%) > water filled pore space (−3.7% to 3.4%) > grain yield (−3.3% to 1.8%) > biomass (−1.6% to 1.4%). Our analysis showed that biochar has a larger impact on environmental outcomes rather than agricultural production. The mechanistic model has the potential to optimize biochar application strategies to enhance environmental and agronomic outcomes but more work is needed to fill knowledge gaps identified in this work.

Modeling Long-Term Corn Yield Response to Nitrogen Rate and Crop Rotation.

Improved prediction of optimal N fertilizer rates for corn (Zea mays L.) can reduce N losses and increase profits. We tested the ability of the Agricultural Production Systems sIMulator (APSIM) to simulate corn and soybean (Glycine max L.) yields, the economic optimum N rate (EONR) using a 16-year field-experiment dataset from central Iowa, USA that included two crop sequences (continuous corn and soybean-corn) and five N fertilizer rates (0, 67, 134, 201, and 268 kg N ha-1) applied to corn. Our objectives were to: (a) quantify model prediction accuracy before and after calibration, and report calibration steps; (b) compare crop model-based techniques in estimating optimal N rate for corn; and (c) utilize the calibrated model to explain factors causing year to year variability in yield and optimal N. Results indicated that the model simulated well long-term crop yields response to N (relative root mean square error, RRMSE of 19.6% before and 12.3% after calibration), which provided strong evidence that important soil and crop processes were accounted for in the model. The prediction of EONR was more complex and had greater uncertainty than the prediction of crop yield (RRMSE of 44.5% before and 36.6% after calibration). For long-term site mean EONR predictions, both calibrated and uncalibrated versions can be used as the 16-year mean differences in EONR’s were within the historical N rate error range (40–50 kg N ha-1). However, for accurate year-by-year simulation of EONR the calibrated version should be used. Model analysis revealed that higher EONR values in years with above normal spring precipitation were caused by an exponential increase in N loss (denitrification and leaching) with precipitation. We concluded that long-term experimental data were valuable in testing and refining APSIM predictions. The model can be used as a tool to assist N management guidelines in the US Midwest and we identified five avenues on how the model can add value toward agronomic, economic, and environmental sustainability.

A Systems Modeling Approach to Forecast Corn Economic Optimum Nitrogen Rate

Historically crop models have been used to evaluate crop yield responses to nitrogen (N) rates after harvest when it is too late for the farmers to make in-season adjustments. We hypothesize that the use of a crop model as an in-season forecast tool will improve current N decision-making. To explore this, we used the Agricultural Production Systems sIMulator (APSIM) calibrated with long-term experimental data for central Iowa, USA (16-years in continuous corn and 15-years in soybean-corn rotation) combined with actual weather data up to a specific crop stage and historical weather data thereafter. The objectives were to: (1) evaluate the accuracy and uncertainty of corn yield and economic optimum N rate (EONR) predictions at four forecast times (planting time, 6th and 12th leaf, and silking phenological stages); (2) determine whether the use of analogous historical weather years based on precipitation and temperature patterns as opposed to using a 35-year dataset could improve the accuracy of the forecast; and (3) quantify the value added by the crop model in predicting annual EONR and yields using the site-mean EONR and the yield at the EONR to benchmark predicted values. Results indicated that the mean corn yield predictions at planting time (R2 = 0.77) using 35-years of historical weather was close to the observed and predicted yield at maturity (R2 = 0.81). Across all forecasting times, the EONR predictions were more accurate in corn-corn than soybean-corn rotation (relative root mean square error, RRMSE, of 25 vs. 45%, respectively). At planting time, the APSIM model predicted the direction of optimum N rates (above, below or at average site-mean EONR) in 62% of the cases examined (n = 31) with an average error range of ±38 kg N ha−1 (22% of the average N rate). Across all forecast times, prediction error of EONR was about three times higher than yield predictions. The use of the 35-year weather record was better than using selected historical weather years to forecast (RRMSE was on average 3% lower). Overall, the proposed approach of using the crop model as a forecasting tool could improve year-to-year predictability of corn yields and optimum N rates. Further improvements in modeling and set-up protocols are needed toward more accurate forecast, especially for extreme weather years with the most significant economic and environmental cost.

Spatial Characterization of Soybean Yield and Quality (Amino Acids, Oil, and Protein) for United States

Continued economic relevancy of soybean is a function of seed quality. The objectives of this study were to: (i) assess the spatial association between soybean yield and quality across major US soybean producing regions, (ii) investigate the relationship between protein, oil, and yield with amino acids (AAs) composition, and (iii) study interrelationship among essential AAs in soybean seed. Data from soybean testing programs conducted across 14 US states from 2012 to 2016 period (n = 35,101 data points) were analyzed. Results indicate that for each Mg ha−1 yield increase, protein yield increased by 0.35 Mg protein ha−1 and oil yield improved by 0.20 Mg oil ha−1. Essential AA concentrations exhibit a spatial autocorrelation and there was a negative relationship between concentration of AA, protein, and oil, with latitude. There was a positive interrelationship with different degree of strength among all AAs, and the correlation between Isoleucine and Valine was the strongest (r = 0.93) followed by the correlation among Arginine, Leucine, Lysine, and Threonine (0.71 < r < 0.88). We concluded that the variability in genotype (G) x management (M) x environment (E) across latitudes influencing yield also affected soybean quality; AA, protein, and oil content in a similar manner.

Simple Models for Describing Ruminant Herbivory

The use of quantitative independent variables in experiments allows the use of regression to explore the functional relationship between treatments applied and measured responses. It provides the opportunity to not only understand the magnitude and importance of the response but also ascertain its nature. The simplest approach is to fit a polynomial. While it is often possible to obtain a very good fit using this approach, it offers in the way of providing insight into the response. At best, you can determine if the response is nonlinear and if so, if it is complex or not. The model parameters are empirical and generally cannot be interpreted as having any biological, chemical, or physical meaning—at least not directly. There are situations, however, when such a meaning can be inferred from a model fit using simple regression. In general, this is true when the relationship is truly linear or when a nonlinear model can be considered to be “intrinsically” linear; that is, it can be linearized by transforming the data in a way that can be fit using simple linear regression. A series of forage quality examples are used to illustrate these concepts in this article.

Commentary on ‘Current economic obstacles to biochar use in agriculture and climate change mitigation’ regarding uncertainty, context-specificity and alternative value sources

ABSTRACT A recent study in Carbon Management, by Bach et al., argues that biochar amendments’ positive impacts on crop yields and soil carbon sequestration have been overestimated, and biochar amendment to soil is hence unlikely to be an economically viable technique for cropping system management or C abatement. We question the data selection and analysis techniques that the authors used to assess the effect of biochar on crop yield, biochar stability in soil, and biochar production cost. Although the research article was not intended as a meta-analysis – and hence the data reported need not be analyzed with the full rigor of a systematic review – we assert that the employed data set, while containing a sufficient quantity of data, requires closer inspection and a more careful interpretation to avoid significant bias in the conclusions. Furthermore, we assert that proper implementation of biochar and inclusion of non-yield benefits in the analysis would render it more economically viable for both cropping system enhancement and C sequestration than portrayed by Bach et al.