J. T. Ritchie

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.

Cereal growth, development and yield

The objective of the CERES crop simulation models is to predict the duration of growth, the average growth rates, and the amount of assimilate partitioned to the economic yield components of the plant. With such a simulation system, optimizing the use of resources and quantifying risk related to weather variation is possible. The cereal crops included in the DSSAT v3 models are maize, wheat, barley, sorghum, millet and rice. A feature of each model is its capability to include cultivar specific information that make possible prediction of the cultivar variations in plant ontogeny and yield component characteristics and their interactions with weather. Biomass growth is calculated using the radiation use efficiency approach; biomass produced is partitioned between leaves, stems, roots, ears and grains. The proportion partitioned to each growing organ is determined by the stage of development and general growing conditions. The partitioning principles are based on a sink source concept and are modified when deficiencies of water and nutrient supplies occur. Crop yields in the CERES models are determined as a product of the grain numbers per plant times the average kernel weight at physiological maturity. The grain numbers are calculated from the above ground biomass growth during a critical stage in the plant growth cycle for a fixed thermal time before anthesis. The grain weight in all the CERES models is calculated as a function of cultivar specific optimum growth rate multiplied by the duration of grain filling. Grain filling is reduced below the optimum value when there is an insufficient supply of assimilate from either the daily biomass production or from stored mobile biomass in the stem. The CERES models have been tested over a wide range of environments. Although there are improvements that can be made in the simulation procedures, results have shown that when the weather, cultivar and management information is reasonably quantified, the yield results are usually within acceptable limits of ±5% to 15% of measured yields.

Soil water balance and plant water stress

The soil water balance is calculated in the DSSAT crop models in order to evaluate the possible yield reduction caused by soil and plant water deficits. The model evaluates the soil water balance of a crop or fallow land on a daily basis as a function of precipitation, irrigation, transpiration, soil evaporation, runoff and drainage from the profile. The soil water is distributed in several layers with depth increments specified by the user. Water content in any soil layer can decrease by soil evaporation, root absorption, or flow to an adjacent layer. The limits to which water can increase or decrease are input for each soil layer as the saturated upper limit. The values used for these limits must be appropriate to the soil in the field, and accurate values are quite important in situations where the water input supply is marginal.

A comparison of the models AFRCWHEAT2, CERES-Wheat, Sirius, SUCROS2 and SWHEAT with measurements from wheat grown under drought

The predictions of five simulation models were compared with data from a winter sown wheat experiment performed in a mobile automatic rainshelter at Lincoln, New Zealand in 1991/1992, where observed grain yields ranged from 3.6 to 9.9 t ha−1. Four of the five models predicted the yield of the fully irrigated treatment to within 10%, and SWHEAT underestimated by more than 20%. The same four models also predicted the grain yield response to varying water supply with reasonable accuracy, but SWHEAT again underestimated the yield reduction with increasing drought. However, the performance of all the models in predicting both the time course and final amount of aboveground biomass, of leaf area index (LAI) and evapotranspiration, varied substantially. These variations were associated with their diverging assumptions about the effects of root distribution and soil dryness on the ability of the crops to extract water, the value of the ratio of water supply to water demand at which stress begins to reduce leaf area development, and photosynthetic, or light-use efficiency (LUE). All the models predicted, to varying degrees, that reductions in photosynthetic efficiency or LUE was an important contributor to reductions in the rate of biomass accumulation. In contrast, analysis of the experimental data indicated that this factor was a minor contributor to the reduction, and variation in light interception, associated with changes in LAI, was the major cause.

Water dynamics in the soil-plant-atmosphere system

The water problem in agriculture is related both to weather and to the reserves of water in the soil that are available to plants. Water dynamics in the soil-plantatmosphere system concerns the capacity of the soil water reservoir, its depletion and replenishment, and its efficient management for crop production. The concept of the soil as a reservoir for water is appealing and useful. Since only a small amount of water can be stored in crop plants relative to the rate of transpiration through them, it is the storage of water within the soil pores that permits transpiration to continue for several days without recharge by rainfall or irrigation. However, water storage in the soil is not similar to that in a bucket. Some water may drain out of the root zone, and not all water remaining in a drying soft can be taken up by the plant as rapidly as it is needed because it is held too tightly by soil particles. Although methods of determining the capacity of the soil water reservoir available to the plant are not exact, the concept permits calculations of the soil water balance and its impact on crop production. Water-balance calculations using computers are becoming more common. There should be more emphasis on water-balance technology in the future because it is needed for accurate estimation of crop yields, early warning about food shortages, better farm management, reliable irrigation scheduling and water-resource planning, etc. Because of these urgent needs, it is important to develop models of the water balance that are as general as possible so that local calibrations are eliminated or at least minimized. Models should also not depend on the input of weather records that are difficult to obtain. The dynamics of the soil-water balance requires separate understandings of the atmospheric, plant and soil-water factors which affect the soil water balance. These factors are interdependent but will be discussed separately for simplicity.

Spatial validation of crop models for precision agriculture

Abstract Spatial measurements of yield using technological advances like on-the-go yield monitoring systems have clearly shown large within-field variability in crop yields suggesting that field yields could be increased or cost decreased by varying management over space. This study evaluated the utility of the CROPGRO-Soybean simulation model and remote sensing in the interpretation of a soybean yield map. CROPGRO was executed on areas within the field defined as reasonably uniform by a Normalized Difference Vegetative Index (NDVI) analysis. The model was able to closely predict the crop yield variability measured within the field when the measured soil type and plant population were used as model inputs. Remote sensing was useful in finding spatial patterns across the field to target sampling and to provide spatial inputs for the model. Results of this study showed that a combination of crop model and remote sensing can identify management zones and causes for yield variability, which are prerequisites for zone-specific management prescriptions.

Root growth and water uptake during water deficit and recovering in wheat

Root growth and soil water content were measured in a field experiment with wheat subjected to two periods of water deficit. The first period was induced early in the season between the early vegetative stage (22 DAS) and late terminal spikelet (50 DAS), the second period at mid-season between terminal spikelet (42 DAS) and anthesis (74 DAS). Total root growth was reduced under water deficit by a reduction in the top 30 cm, while the root system continued to grow in the deeper soil profile between 30 and 60 cm. Shortly after rewatering, the growth pattern reverted to fastest root growth rates in the shallow soil layers. In relative terms, the total root system increased in relation to the above ground dry matter under water shortage. The early-, the mid-season water deficit treatments, and the control treatment had total root length of 27.4, 19.4 and 30.6 km m-2, respectively, about 2 wk before maturity. Evapotranspiration declined under water deficit, but water uptake in deeper layers increased. Water uptake per unit root length was reduced with water deficit and was still low shortly after rewatering. Remarkable was the increase in water uptake at 2–3 weeks after rewatering, both deficit treatments exceeded the control by almost 100%. This increase in water uptake followed the burst of new root growth in the upper regions of the soil. However, water uptake rates subsequently declined towards maturity, being between 0.15 L km-1 d-1 and 0.17 L km-1 d-1 for the early and mid-season water deficit treatments, slightly higher than the control, 0.12 L km-1 d-1. The results showed that the crop subjected to early water deficit could compensate for some of the reductions in root growth during subsequent rewatering, but the impact of the mid-season water deficit treatment was more severe and permanent.

Effects of soil water-deficits during tassel emergence on development and yield component of maize (Zea mays)☆

Abstract Water-deficits during flowering and pollination can cause considerable reductions in yields of determinate crops due to influences on reproductive components. This study was conducted to determine how different levels of soil water-deficit during emergence of tassels influence silking and yield components of maize. A rain shelter provided timely water-deficits in a field environment during 1987 on a sandy soil in Michigan. Yield reductions in excess of 90% occurred when water-deficits spanned the interval from just prior to tassel emergence to beginning of grain-fill. Emergence of tassels and silks was delayed more than two weeks. Final internode lengths reflected reductions in plant extension growth, and evaluation of lengths of individual internodes depicted the windows of development in which plants were most affected by water-deficits. Grain number was reduced in proportion to the duration of the water-deficit period. Assessment of grain and non-grain above-ground biomass 100 days after sowing demonstrated that both were reduced by severe water-deficits, but not to the same degree. There was delayed development in response to the severe water-deficits, as was apparent from 75% silking dates and biomass assessments.

Simple model to estimate field-measured soil water limits

Engineering and modeling applications often require reasonable estimates of the upper and lower limits of plant extractable water. Laboratory measurements of these limits do not always coincide with field observations. Statistical models to estimate soil water retention can be complicated, and usually are not based on field measurements. The objective of this work was to develop simple, generic equations to estimate the field-measured limits of the soil water reservoir based on soil survey data such as texture and bulk density. We used linear regression to estimate the gravimetric upper limit from sand and clay content; the volumetric upper limit can be estimated from the gravimetric upper limit and bulk density. We used non-linear regression to estimate the volumetric plant extractable water from sand content. The lower limit can be estimated as the difference between the upper limit and the plant extractable water. We adjusted the predictions for coarse fragments and organic C and we placed reasonable upper and lower boundaries to our estimates. The equation for the drained upper limit has a RMSE of 0.030 kg kg–1 and uses two coefficients. The equation for the plant extractable water has a RMSE of 0.030 m3 m–3 and uses three coefficients. We tested the simple model with an independent data set. The RMSE of our model was similar or better than that of a more complex statistical model.

A Dynamic Grain Sorghum Growth Model

A grain sorghum growth model based on complex physical, physiological and morphological principles simulated plant DM accumulation relatively accurately. Changes in row spacing, row direction, plant population, hybrids, ambient temperature, daily solar radiation or available soil moisture resulted in changes in plant DM accumulation