Frank D. Whisler

Crop simulation models in agronomic systems

Publisher Summary This chapter discusses some crop simulation models in agronomic systems. Many crop models or parts of crop models have been built to help the researcher and students understand the operation of some part of an agronomic cropping system, for example, soil water flow, stomata1 control, or fertilizer nutrient movement. In addition to understanding various parts of agronomic systems, the modelers want to see what can be expected to happen if some change is made in that system. Field tests are very expensive, especially as the numbers of variables and/or treatments increase, and years of results are needed. A proven model of the system helps to evaluate these treatments and indicates which ones could be expected to give the desired results. The cotton model, GOSSYM, has been widely validated. GOSSYM has been used in a user-friendly form on a PC microcomputer as a tool for on-farm management decisions pertaining to nitrogen fertilizer applications, irrigation scheduling, and timing of harvest-aid chemicals. By combining GOSSYM with an expert system program, COMAX, the on-farm management decisions have been run in several combinations to give the user an optimal plan for fertilizer and irrigation scheduling.

Application of the GOSSYM/COMAX system to cotton crop management

Abstract The GOSSYM/COMAX model-based-reasoning system, a decision aid for cotton crop management, has been extensively tested over the last five years on commercial and research farms across the Cotton Belt in the southern United States. This paper gives a brief description of the cotton crop model GOSSYM and the companion expert system COMAX, reports the results of these extensive field tests, and gives projections for future enhancements of the system.

Indirect estimation of soil hydraulic properties to predict soybean yield using GLYCIM

GLYCIM, a mechanistic model of soybean (Glycine Max L.) growth and development, requires soil hydraulic parameters as input. These data are usually not readily available. The objective of this study alas to compare yields calculated with measured hydraulic properties to those calculated with hydraulic properties estimated from soil texture and bulk density. We reviewed estimation methods and chose two methods to estimate a soil moisture release function and two methods to obtain saturated hydraulic conductivity. Both methods use soil texture and bulk densit), as predictors. Soil water retention predicted bJ3 these methods correlated ,t,ell with measured soil water retention whereas the estimation of saturated hydraulic conductivity was poor. Soybean yields were simulated using GLYCIM with and without irrigation ,for seven locations in Mississippi. USA, using seven years of weather records. Simulated Isields lz’ere aJTected more by the method of estimating the moisture release curve than b>, the method of estimating saturated hJ,draulic conductivity. The average simulated yields from estimated properties tz‘ere higher than those from measured properties because estimated bllater retention provided more availuble water. Correlation betbtleen yields simulated using measured and estimated hydraulic properties was higher under non-irrigated conditions than ti,ith irrigation. Averaging I’ields over years lcith diflerent lveather conditions greatl_v improved the correlations. Published b?, Elserier Science Ltd

Error analysis of soil temperature simulations using measured and estimated hourly weather data with 2DSOIL

Many crop simulation models use 1-h time steps for atmospheric, soil and plant processes but often meteorological data are only available as daily summaries. The objective of this study was to investigate how errors in estimation of hourly values of solar radiation and air temperature affect errors in simulation of soil temperature using the model 2DSOIL. 2DSOIL is a two-dimensional finite element model that simulates water flow, chemical, water and solute uptake by plant roots, chemical equilibria processes, and gas and heat transport in soil. The standard deviations of the differences between hourly estimated air temperatures were about 2 � Ca nd 85 Wm � 2 for solar radiation. The mean difference in simulated and measured soil temperatures using measured hourly weather data for all depths at both sites was less than 1 � C and standard deviations were about 1–3 � C indicating low bias. The range of errors was highest in the surface soils when the soil was wetted after rainfall. Relative to simulated soil temperatures using measured hourly data, simulated soil temperatures using estimated data were, on average over all depths, 2 � C lower and standard deviations ranged from 2 to 3 � C. The errors were similar over all depths. Use of estimated hourly air temperature and radiation generally resulted in underpredictions of soil temperature by 2–3 � C and increased error. Also maximum daily soil temperatures were underestimated. Published by Elsevier Science Ltd.

Crop management and input optimization with GLYCIM: differing cultivars

The soybean simulation model GLYCIM is mechanistic and operates at the physical and physiological process level. The model is organized in modules along disciplinary lines using a generic modular structure. The model was initially validated with data sets collected at the Plant Science Farm at Mississippi State University and calibrated to the cultivar “Forest”. In 1991 GLYCIM was released to soybean farmers and scientists at state experimental stations for crop management and input optimization. The soybean growers have claimed a 14-29% increase in yields and over a 400% increase in irrigation efficiency from using GLYCIM to manage irrigation. During the past two years, significant changes to GLYCIM have improved its predictions. Model improvements pertain to the effect of water stress on several physiological processes in the soybean plant and the method of accounting for numbers and weights of individual pods and seeds. In addition, cultivar parameter files have been developed for several cultivars. As a result of these changes, improved simulations have been obtained and grower usage of the model has been enhanced. The development of cultivar parameter files and the resulting simulations are discussed.

Analysis of the effects of herbicides on cotton yield trends

Abstract Cotton ( Gossypium hirsutum L.) yields have declined since 1965 despite improvements in technology and introductions of higher yielding cultivars. Cotton scientists have been unable to identify exact causes of the yield reduction. As part of the effort to examine possible causes for the yield reduction problem, the cotton crop simulation model, GOSSYM, was used to analyze the effects of root damage caused by herbicides on cotton yield trends. Weather, soil and cultural input data taken at five locations for over 20 years were acquired for this study. Simulations were completed for crops grown at the cotton breeders variety trial locations at Florence, SC; Stoneville, MS; College Station, TX; Phoenix, AZ; and Fresno, CA. The simulated adverse effects of the herbicides on cotton yields varied from location to location due to their interactions with soil, plant and atmospheric variables. Cooler temperatures in Stoneville, MS, intensified herbicide damage, while the long growing season and warm climate in Fresno, CA, reduced the adverse effects of herbicide damage. Increased N and water shortages were apparent with herbicide induced root pruning. The effect of the herbicides on simulated cotton yield reduction was highest at Stoneville, MS, showing a decrease of 137 kg ha −1 . The lowest yield reductions (14 kg ha −1 ) were observed at Fresno, CA, due to herbicidal action on cotton roots. The effect of herbicides on cotton yield reduction was found to be greater under unfavorable weather conditions like cooler temperatures during the boll-opening period.

Analysis of the effects of soil compaction on cotton yield trends

Abstract Cotton (Gossypium hirsutum L.), yields in the U.S. Cotton Belt declined from 1960 to 1980 despite improvements in technology and introductions of higher yielding cultivars. As part of the effort to examine possible causes for the yield reduction, the cotton crop simulation model, GOSSYM, was used to analyze the effects of soil compaction on cotton yield trends. Weather, soil and cultural input data from six locations over 20 years were acquired and used or this study. There were no consistent trends over all locations. Prior to 1974, compaction had some negative effect at Florence, South Carolina, but due to annual in-row subsoiling, had no effect after that time. At Stoneville, Mississippi, the effects of compactions were generally detrimental but they were often masked by weather. In years of abundant moisture, wheel traffic compaction had little negative effect on yields, since shallow root systems could extract sufficient moisture for plant growth and yield. In extremely dry years, predicted yields were low for both compacted and uncompacted crops. The effect of wheel compaction on yield was generally favorable at College Station, Texas. The lower yielding crop, however, generally put more of its photosynthate into roots during the boll filling period. This was also true at Phoenix, Arizona, where the results were erratic. At Lubbock, Texas, on a clay soil the effects of simulated compaction were negligible.

Validation of the soil-temperature model of GOSSYM-COMAX

Abstract Soil temperature in GOSSYM, a cotton simulation model, is calculated in the TMPSOL subroutine, which is an empirical model. To validate TMPSOL, soil-temperature data were collected in the field for 2 years, at two locations (row and furrow) and four depths (5, 10, 25 and 60 cm) under bare and cotton cropped surface conditions. Under bare surface conditions, TMPSOL adequately predicted the average daily soil temperature only at the 5 and 10 cm depths of the row, and behaved irregularly in the furrow at those depths. At the 25 and 60 cm depths, TMPSOL underpredicted the soil temperatures in both row and furrow. Under cotton cropped surface conditions, before canopy closure, the results seemed to be similar to those of the bare surface conditions. After canopy closure, TMPSOL significantly overestimates the soil temperature at all locations and depths, sometimes by as much as 5°C. Implementation of a more mechanistic soil temperature model for GOSSYM is recommended.

An analysis of the impact of lygus on cotton.

Validation of the cotton simulation model GOSSYM against a commercial crop grown in 1976 at Scott, Mississippi, revealed that a 32% yield loss had occurred due to lygus (Lygus lineolaris) (Palisot de Beauvois) damage to squares and to fruiting branch development at the eighth and ninth nodes on the plant. Model crops grown under soil and weather conditions at Mississippi State University with various rates of lygus infestation at 0, 11 115, and 22 230 insects per hectare indicate that yield loss due to damage beginning the first week of squaring may be quite variable (7% to 60%) depending on the insect population. The higher insect population delayed crop maturity by two weeks. Simulated yield loss due to damage beginning after the first week of squaring was much less variable averaging about 25%.

Spatial validation of cotton simulation model in relation to soils and multispectral imagery

Field studies were conducted in 1998 and 1999 in Livingston Field at Perthshire Farm, Bolivar County which is located in west-central Mississippi along the Mississippi River. It is a 162 ha field and has a 2-m elevation range. The dominant soil series of the field are Commerce silt loam, Robinsonville fine sandy loam and Souva silty clay loam. The objectives of the study were to (1) compare GOSSYM simulated yield with actual yield, (2) study spatial and temporal pattern of cotton crop across two growing seasons using multispectral imagery, 3) predict field based lint yield from remote sensed data, and determine age of the crop most suitable for aerial image acquisition in predicting yield and/or discriminating differences in cotton growth. Two transects were selected for GOSSYM study, each containing twelve sites. A 1-m length of single row plot was established at each profile. Plant mapping was done five times in 1998 and seven times in 1999 growing seasons. GOSSYM simulation runs were made for each profile and compared with actual crop parameters using root mean square error (RMSE). Results based on averaging common soil mapping units indicate that GOSSYM accuracy in predicting yield varied from 0.45 bales acre-1 to 0.61 bales acre-1. To monitor and estimate the biophysical condition of the cotton crop, airborne multispectral images were acquired on 10 dates in 1998 and 17 dates in 1999 from April to September. In both years site-specific yield and normalized difference vegetation index (NDVI) were significantly (p < 0.0001) correlated in July. Changes in NDVI in 1999 across sampling dates for the different sites showed the least distinctiveness due to somewhat wetter weather conditions, as compared to drier weather in 1998. Crop growing in or near the drainage areas were low in NDVI and lint yield. Multispectral images acquired between ~ 300 - 600 growing degree days above 60°C (GDD60) may be useful decision tools for replanting certain areas of the field, especially in dry weather conditions when variability in crop growth pattern is enhanced due to spatial variability in soil texture, which influences the capacity of a soil to hold moisture and to release it to plants for growth. Results suggest that 2-3 multispectral images acquired between 800 and 1500 GDD60 can be used to monitor crop health and predict yield in a normal weather condition.