W. D. Batchelor

The DSSAT cropping system model

The decision support system for agrotechnology transfer (DSSAT) has been in use for the last 15 years by researchers worldwide. This package incorporates models of 16 different crops with software that facilitates the evaluation and application of the crop models for different purposes. Over the last few years, it has become increasingly difficult to maintain the DSSAT crop models, partly due to fact that there were different sets of computer code for different crops with little attention to software design at the level of crop models themselves. Thus, the DSSAT crop models have been re-designed and programmed to facilitate more efficient incorporation of new scientific advances, applications, documentation and maintenance. The basis for the new DSSAT cropping system model (CSM) design is a modular structure in which components separate along scientific discipline lines and are structured to allow easy replacement or addition of modules. It has one Soil module, a Crop Template module which can simulate different crops by defining species input files, an interface to add individual crop models if they have the same design and interface, a Weather module, and a module for dealing with competition for light and water among the soil, plants, and atmosphere. It is also designed for incorporation into various application packages, ranging from those that help researchers adapt and test the CSM to those that operate the DSSAT-CSM to simulate production over time and space for different purposes. In this paper, we describe this new DSSAT-CSM design as well as approaches used to model the primary scientific components (soil, crop, weather, and management). In addition, the paper describes data requirements and methods used for model evaluation. We provide an overview of the hundreds of published studies in which the DSSAT crop models have been used for various applications. The benefits of the new, re-designed DSSAT-CSM will provide considerable opportunities to its developers and others in the scientific community for greater cooperation in interdisciplinary research and in the application of knowledge to solve problems at field, farm, and higher levels.

Examples of strategies to analyze spatial and temporal yield variability using crop models

Process-oriented crop growth models simulate plant growth over homogeneous areas. The advent of precision farming has resulted in the need to extend the use of point-based crop models to account for spatial processes. Spatial processes include surface and subsurface water flow and spatial and temporal interaction of plant growth with soil water, nutrient and pest stress and management practices. Our research has focused on developing methods to account for spatial interactions in the CROPGRO-Soybean and CERES-Maize models. These methods introduce new challenges for accurately and economically defining spatial inputs for the models. In spite of these challenges, both models have been used to evaluate causes of spatial yield variability with reasonable success. The purpose of this paper is to present several examples of strategies that we have found useful in using these models to assess spatial and temporal yield variability over different environmental conditions and to analyze economic return of prescriptions. Strategies to overcome spatial resolution in point-based crop models include calibration techniques to run point-based models at small scales within a field, using remote sensing to target measurements of models inputs to areas of similar plant response, and linking point-based models to three-dimensional water flow models to better represent water transport. Each strategy is demonstrated using case studies and comparison of simulated and measured data are presented. A method to estimate break-even costs associated with variable soybean cultivar placement in a field is outlined and presented as a case study as well. Crop models can provide useful estimates of potential economic return of prescriptions, as well as estimate the sensitivity of a prescription to weather. They can also estimate the value of weather information on management prescriptions.

ANALYSIS OFWATER STRESS EFFECTS CAUSING SPATIAL YIELD VARIABILITY IN SOYBEANS

Soybean yields have been shown to be highly variable across fields. Past efforts to correlate yield in small sections of fields to soil type, elevation, fertility, and other factors in an attempt to characterize yield variability has had limited success. In this article, we demonstrate how a process oriented crop growth model (CROPGRO-Soybean) can be used to characterize spatial yield variability of soybeans, and to test hypotheses related to causes of yield variability. In this case, the model was used to test the hypothesis that variability in water stress corresponds well with final soybean yield variability within a field. Soil parameters in the model related to rooting depth and hydraulic conductivity were calibrated in each of 224 grids in a 16-ha field in Iowa using three years of yield data. In the best case, water stress explained 69% of the variability in yield for all grids over three years. The root mean square error was 286 kg ha–1 representing approximately 12% of the three-year mean measured yield. Results could further be improved by including factors that were not measured, such as plant population, disease, and accurate computation of surface water run on into grids. Results of this research show that it is important to include measurements of soil moisture holding capacity, and drainage characteristics, as well as root depth as data layers that should be considered in any data collection effort.

Evaluation of the CERES-Maize water and nitrogen balances under tile-drained conditions

The CERES-Maize model was developed to investigate how variations in environmental conditions, management decisions, and genetics interact to aAect crop development and growth. A tile drainage subroutine was incorporated into CERES-Maize to improve soil-water and nitrogen leaching under subsurface tile drainage conditions. The purpose of this work was to evaluate the soil-water, soil-nitrogen, tile drainage, and tile-nitrogen loss routines of CERES-Maize for tile-drained fields in Iowa. An analysis was conducted based on information collected from a study of 36 plots consisting of five management systems during a 4-year period from 1993 to 1996, at Nashua, IA. The model was calibrated for each plot using data from 1994 and 1995, and validated using data from 1993 and 1996. Temporal soil-water contents and water flow from tile drains were calibrated to an average root mean square error (RMSE) of 0.036 cm 3 cm ˇ3 and 2.62 cm, respectively, compared to measured values. Validation trials gave an average RMSE for soil-water and tile drainage of 0.046 cm 3 cm ˇ3 and 5.3 cm, respectively. Soil-nitrate and tile-nitrogen flows were calibrated, with an RMSE of 6.27 m gN O3 g ˇ1 soil ˇ1 and 3.21 kg N ha ˇ1 soil ˇ1 , respectively. For the validation trials, the RMSE for soil-nitrate content and cumulative tile-nitrate flow was 6.82 m gN O3 g ˇ1 soil ˇ1 and 8.8 kg N ha ˇ1 , respectively. These results indicate that the new tile drainage algorithms describe water and nitrate movement reasonably well, which will improve the performance of CERES-Maize for artificially drained fields. # 2000 Elsevier Science Ltd. All rights reserved.

Simulation of multiple species pest damage in rice using CERES-rice

Abstract Conventional approaches of using empirical equations for quantifying yield losses due to rice pests are limited in their scope and application since these equations are data-specific and insensitive to variable cropping and pest conditions. Recent developments in crop growth modelling provide a physiologically based approach to simulate pest damage and crop interactions. A generic approach to simulate the damage effects of single or multiple pests is presented using the CERES-rice crop growth model. Pest or damage levels from field scouting reports can be entered and damage is applied to appropriate physiological coupling points within the crop growth model including leaf area index, stand density, intercepted light, photosynthesis, assimilate amount and translocation rate, growth of different plant organs and leaf senescence. Equations and algorithms were developed to describe competition among multiple pests and to link the computed total damage to the corresponding variables of the crop model. A sensitivity analysis was conducted for various individual damage coupling points and for the damage effects of defoliators, weed competition, leaf blast and sheath blight disease. The general response of the model compared favorably with values reported in the literature. The model was also tested for sensitivity to multiple damage. Multiple pest damage usually had less-than-additive effects on yield loss. The approach provides a basis to explore dynamic pest and crop interactions in determining pest management strategies which minimize yield loss.

Tools for optimizing management of spatially-variable fields

Abstract Efficient use of agro-chemicals is beneficial for farmers as well as for the environment. Spatial and temporal optimization of farm management will increase productivity or reduce the amount of agro-chemicals. This type of management is referred to as Precision Agriculture. Traditional management implicitly considers any field to be a homogeneous unit for management: fertilization, tillage and crop protection measures, for example, are not varied within a single field. The question for management is what to do when . Because of the variability within the field, this implies inefficient use of resources. Precision agriculture defines different management practices to be applied within single, variable fields, potentially reducing costs and limiting adverse environmental side effects. The question is not only what and when but also where . Many tools for management and analysis of spatial variable fields have been developed. In this paper, tools for managing spatial variability are demonstrated in combination with tools to optimize management in environmental and economic terms. The tools are illustrated on five case studies ranging from (1) a low technology approach using participatory mapping to derive fertilizer recommendations for resource-poor farmers in Embu, Kenya, (2) an example of backward modelling to analyze fertilizer applications and restrict nitrogen losses to the groundwater in the Wieringermeer in The Netherlands, (3) a low-tech approach of precision agriculture, developed for a banana plantation in Costa Rica to achieve higher input use efficiency and insight in spatial and temporal variation, (4) a high-tech, forward modelling approach to derive fertilizer recommendations for management units in Zuidland in The Netherlands, and (5) a high-tech, backward modelling approach to detect the relative effects of several stress factors on soybean yield.

Enhancing the ability of CERES-Maize to compute light capture

Abstract Recently it has been proposed to use the relationship between average intercepted photosynthetically active radiation (IPAR) around silking and total number of seeds per plant as the basis to improve kernel number prediction in CERES-Maize. However, there has been no previous evaluation of the accuracy of IPAR predictions in the model. The objectives of this work were to evaluate CERES-Maize predictions of IPAR around silking by testing their components incident PAR, light extinction coefficient (k), and leaf area index (LAI), and to develop alternative methods to simulate PAR and k. Measured IPAR was averaged over a thermal time window of 250 before to 100 growing degree-days after silking and compared with model predicted IPAR averaged over the same thermal time window. Independent data sets were used to develop and test new relationships to predict fraction of PAR to solar radiation as a function of incident total solar radiation, and canopy extinction coefficient as a function of the crop phenological age. The new relationships were incorporated into CERES-Maize and the new IPAR predictions were compared with measured values. We found that the common assumption of predicting PAR as 50% of the solar radiation overestimated PAR for our conditions in Iowa, where a value of 43% worked better. The extinction coefficient changed with crop development, reaching a peak of 0.66 at silking, and being lower early and late in the season. The best IPAR predictions were obtained when the new procedures to convert solar radiation into PAR and estimate k were coupled with the original leaf area model of CERES-Maize.

ESTIMATING SPATIALLY VARIABLE SOIL PROPERTIES FOR APPLICATION OF CROP MODELS IN PRECISION FARMING

Crop models have been useful for identifying underlying causes of yield variability and evaluating management prescriptions. However, estimating the spatial soil inputs required to calibrate crop models to historic yields has proven to be challenging and time consuming. Currently, calibration techniques require excessive computer time when applied over many grid points within a field, and procedures for estimating unknown inputs are not well defined. The objectives of this research were: (1) to develop an efficient procedure for estimating spatially variable soil properties for the CROPGRO–Soybean model, and (2) to demonstrate its use in diagnosing areas in the field where excess water or water stress reduce soybean yield. A study was conducted for a 12–ha field in Linn County, Iowa, using soybean data collected during two years (1996 and 1998). Yield, soil type, topography, and soil characterization data were used to estimate spatial variations in soil drainage factors (saturated hydraulic conductivity of an impeding layer and tile drainage spacing), water availability (SCS curve number and maximum rooting depth), and a soil fertility factor. A procedure was developed to create a database of predicted yields for combinations of coefficients, and to search the database using rules based on soil classification, drainage class, and topography to guide the parameter estimation process. When rules based on drainage class were used, the CROPGRO–Soybean model explained 45% to 70% of the yield variability for 1996 and 1998, respectively. When rules based on soil water availability, drainage characteristics, and topography were used, good predictions were obtained in both years (r2 = 0.70 for 1996 and 0.80 for 1998), and RMSE was 2.8% of grid level yields. The data base approach required less than half the time that simulated annealing required for the field with 48 grids.

Incorporating tillage effects into a soybean model.

Crop growth models can be useful tools in evaluating the impacts of different tillage systems on the growth and final yield of crops. A tillage model was incorporated into CROPGRO-Soybean and tested for conditions in Ames, IA, USA. Predictions of changes in surface residue, bulk density, hydraulic conductivity, runoff curve number, and surface albedo were consistent with expected behaviors of these parameters as described in the literature. For conditions at Ames, IA, the model gave good predictions of soil temperature at 6 cm depth under moldboard (R2=0.81), chisel plow (R2=0.72), and no-till (R2=0.81) for 1997 and was able to simulate cooler soil temperatures and delayed emergence under no-till in early spring. However, measured differences in soil temperature under the three tillage treatments were not statistically significant. Excellent predictions of soybean phenology and biomass accumulation (e.g. R2=0.98, 0.97, and 0.95 for pod weight predictions under moldboard, chisel plow, and no-till, respectively) were obtained in 1997. More importantly, the model satisfactorily predicted relative differences in soybean growth components (canopy height, leaf weight, stem weight, canopy weight, pod weight, and number of nodes) among tillage treatments for critical vegetative and reproductive stages in one season. The tillage model was further tested using weather and soybean yield data from 1995 to 1997 at Nashua, IA. Tillage systems considered were no-till, disk-chisel+field cultivator, and moldboard plow+field cultivator. Predicted yields for the 1996 calibration year were within 1.3% of the measured yields for all three tillage treatments. The model gave adequate yield predictions for the no-till (−0.2–3.9% errors), disk-chisel (5.8–6.9% errors), and moldboard (5.5–6.1% errors) tillage treatments for the two years of validation. A sensitivity analysis showed that predicted soybean yield and canopy weight were only slightly sensitive to the tillage parameters (less than 3% change with 30% change in tillage parameters). The model predicted lower yields under no-till for nine out of 10 years of weather at Ames, IA, primarily due to delayed emergence. Yield under no-till was higher for one of the years (a drought year) when no-till had better water conservation and negligible delays in emergence.

EXTENDING THE USE OF CROP MODELS TO STUDY PEST DAMAGE

Most crop growth models do not account for damage caused by pests. This limitation must be removed if the models are to provide useful predictions of production under real farm conditions. A generic framework was developed to couple pest damage of various types into the PNUTGRO and SOYGRO models for peanut and soybean, respectively. Coupling points were identified in the models for applying damage to leaves, stems, roots, pods, seeds, whole plants, and to the supply of assimilate. The resulting models were tested by simulating crops with measured pest damage levels for peanut (foliar disease) and soybean (foliar feeding insects) and comparing observed and simulated crop growth and yield results. This approach for coupling pests with crop models has potential for extending the practical applications of crop models to a broad range of problems.