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Process-based agricultural models, applied in novel ways, can reproduce historical crop yield anomalies in the US, with median absolute deviation from observations of 6.7% at national-level and 11% at state-level. In seasons for which drought is the overriding factor, performance is further improved. Historical counterfactual scenarios for the 1988 and 2012 droughts show that changes in agricultural technologies and management have reduced system-level drought sensitivity in US maize production by about 25% in the intervening years. Finally, we estimate the economic costs of the two droughts in terms of insured and uninsured crop losses in each US county (for a total, adjusted for inflation, of $9 billion in 1988 and $21.6 billion in 2012). We compare these with cost estimates from the counterfactual scenarios and with crop indemnity data where available. Model-based measures are capable of accurately reproducing the direct agro-economic losses associated with extreme drought and can be used to characterize and compare events that occurred under very different conditions. This work suggests new approaches to modeling, monitoring, forecasting, and evaluating drought impacts on agriculture, as well as evaluating technological changes to inform adaptation strategies for future climate change and extreme events.

Scientists have estimated that global crop production needs to double by 2050 to supply the demand for food, feed, and fuel. To reach this goal, novel methods are needed to increase breeding potential yield rates of gain as well as on-farm yields through enhanced management strategies. Both of these tasks require the ability to predict plant performance in multiple, dynamic environments based on a knowledge of cultivar characteristics (critical short day lengths, maximum leaf photosynthetic rates, pod fill durations, etc.) that are ultimately linked to genetics. Because of this linkage, we refer to such traits as genotype-specific parameters (GSP's). Using industry-provided yield and weather data from 353 site-years, we estimated seven primary CROPGRO-Soybean GSP's for each of 182 varieties. The data set had two shortcomings. First, no planting dates were supplied, rendering unknowable the environment actually experienced by the crop. Second, soil data were provided only for the top 20 cm, which is inadequate to specify the root environment and water supply availability. Therefore, additional edaphic information was acquired. A novel optimization algorithm was developed that simultaneously estimates GSP's and planting dates, while tuning layered soil water-holding properties. The optimizer, which we have named the holographic genetic algorithm (HGA), uses both externally supplied constraints and its own analysis of data structure to reduce what would otherwise be a search over 2000 dimensions to a much smaller number of overlapping 1- to 3-D problems. Two types of runs were performed. The first was preceded by an independent component analysis (ICA) of published GSP's. The subsequent training sought good component scores rather than the GSP's themselves. The second, separate factor (SF) approach allowed all GSP's to vary separately. This makes parameters unconstrained and more evenly distributed. Results showed that HGA works quite well with the CROPGRO-Soybean model to estimate the cultivar and site-specific parameters from breeding trial data. The quality of the calibrations and evaluations were similar across both run types with RMSE values being ca. 5.6% of the maximum yields. Moreover, the GSP's for a variety can be used to predict its yield in trials not used in that cultivar's calibration. Finally, despite high dimensionality, the GSP's, planting dates, and soil properties for all lines and sites converged concurrently in

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., Highlights • 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

Plant breeding applications of crop simulation models require cultivar coefficients of new breeding lines. These cultivar coefficients are normally estimated from field experiments conducted under optimum conditions over several environments with elaborate data collection for each line throughout its life cycle. Such an intensive sampling scheme poorly applies to breeding lines at the early testing stages, because the number of lines is large and seed supply is limited. The objective of this study was to determine the minimum data to be collected for the estimation of cultivar coefficients of peanut lines for breeding applications of the CSM-CROPGRO-Peanut model. Nine peanut lines varying in maturity were selected for this study. Data on plant growth and development stages of these lines that were collected following the recommended procedure were obtained from a previous study. These data were used in a stepwise procedure to determine the minimum plant characteristics required for deriving the cultivar coefficients. This included both the intensity of data collection as well as the type of phenological and growth characteristics. The full versus partial data sets were then used for model calibration to derive the cultivar coefficients and an independent data set was used for model evaluation. The results showed that (i) different types of reduced phenological data resulted in the same values of the cultivar coefficients; (ii) cultivar coefficients derived from some reduced growth data sets were as good as those derived from full data collection; (iii) model calibration of cultivar coefficients derived from various types of reduced data collection worked well for all development characteristics and fairly well for the normalized root mean square error (RMSEn) values of the plant growth characteristics; and (iv) model evaluation showed good agreements between observed and simulated values for all growth and development characteristics. These results showed that it is possible to reduce the data collection for cultivar coefficients determination. The minimum data suggested is to determine two developmental stages, i.e., first flowering (R1) and harvest maturity (R8), and three plant samplings for growth analysis, i.e., around the stages of full seed (R6), physiological maturity (R7) and harvest maturity (R8).

Crops such as soybean ( Glycine max L.) are grown predominantly under rainfed conditions where water is a major limiting factor and the interannual variability in rainfall pattern is high. Crop modeling has proven a valuable tool to evaluate the long-term consequences of weather patterns, but the candidate crop models must be tested and calibrated for new regions prior to their use as extrapolation tools to predict optimum cultivar choice and sowing dates. The objectives of this paper were to calibrate the CROPGRO-soybean model for growth and yield under rainfed conditions in Galicia, northwest Spain, and then to use the calibrated model to establish the best sowing dates for three cultivars at three locations in this region. The starting point of the calibration process was the CROPGRO-soybean version previously calibrated for non-limiting water conditions. The original model, when simulated versus rainfed soybean field data sets, tended to simulate more severe water stress than actually occurred. In order to calibrate growth and yield for the actual soil we tried several ways for the modelled crop to have access to more water. Modifications were made on soil depth, water holding capacity, and root elongation rate. In addition, other changes were made to predict accurately the observed water-stress induced acceleration of maturity. Long-term simulations with recorded weather data showed that soybean is more sensitive to planting date under irrigated than rainfed management, in the three studied Galician locations.