K. J. Boote

The CROPGRO model for grain legumes.

The CROPGRO model is a generic crop model based on the SOYGRO, PNUTGRO, and BEANGRO models. In these earlier crop models, many species attributes were specified within the FORTRAN code. CROPGRO has one set of FORTRAN code and all species attributes related to soybean, peanut, or drybean are input from external ‘species’ files. As before, there are also cultivar attribute files. The CROPGRO model is a new generation model in several other ways. It computes canopy photosynthesis at hourly time steps using leaf-level photosynthesis parameters and hedge-row light interception calculations. This hedgerow approach gives more realistic response to row spacing and plant density. The hourly leaf-level photosynthesis calculations allow more mechanistic response to climatic factors as well as facilitating model analysis with respect to plant physiological factors. There are several evapotranspiration options including the Priestley-Taylor and FAO-Penman. An important new feature is the inclusion of complete soilplant N balance, with N uptake and N2-fixation, as well as N deficiency effects on photosynthetic, vegetative and seed growth processes. The N2-fixation option also interacts with the modeled carbohydrate dynamics of the plant. CROPGRO has improved phenology prediction based on newly-optimized coefficients, and a more flexible approach that allows crop development during various growth phases to be differentially sensitive to temperature, photoperiod, water deficit, and N stresses. The model has improved graphics and sensitivity analysis options to evaluate management, climate, genotypic, and pest damage factors. Sensitivity of growth processes and seed yield to climatic factors (temperature, CO2, irradiance, and water supply) and cultural management (planting date and row spacing) are illustrated.

Modeling Soybean Growth for Crop Management

ABSTRACT Asoybean (Glycine max (L.) Merr.) crop growth simulation model (SOYGRO) was developed to aid farm managers in making irrigation and pest management decisions. Non-linear first order differential equations describe dry matter rates of change, accumulation and depletion of protein pools, and changes in shell and seed numbers. Two data sets from defoliation and irrigation experiments were used for calibration and validation of the model. The model responds well to drought and defoliation stresses for two test cases. Sensitivity analyses of SOYGRO revealed that simulated yield was most sensitive to changes in gross photosynthesis and growth respiration. The sensitivity of simulated yield to changes in model parameters was increased by the occurrence of either water or defoliation stress.

Physiology and modelling of traits in crop plants: implications for genetic improvement

Abstract Crop growth models have excellent potential for evaluating genetic improvement, for analyzing past genetic improvement from experimental data, and for proposing plant ideotypes for target environments. Crop models used for these plant breeding applications should be sufficiently mechanistic that processes can be investigated in a manner familiar to crop physiologists and plant breeders. In addition, the crop models must consider a sufficient number of cultivar-specific traits descriptive of life cycle phases, vegetative traits, and reproductive growth attributes. In this paper, we discuss how crop models consider genetic variability within a species (cultivar variation), how varietal characteristics can be determined from variety trial or other data, how crop models can be used to evaluate past genetic improvement, and how crop models can be used to hypothesize ideotypes for specific environments. We conclude that crop growth models can partially reproduce genotype by environment interactions when considered across broad ranges of weather and sites, and that crop models can be used to help plant breeders target cultivar improvement for specific environments. However, more physiological insight into primary processes such as source–sink relationships and morphological development will be needed for enhanced application of the models in breeding programmes.

Modeling Growth, Development, and Yield of Grain Legumes using Soygro, Pnutgro, and Beangro: A Review

The interactions between plants and their environment involve an elaborate collection of biological, physical, and chemical processes. To better understand the responses of crops to their environments, computer models are being used to study both the simple and complex aspects of this system.

Response of rice to carbon dioxide and temperature

Abstract The current increase in atmospheric carbon dioxide concentration ([CO 2 ]) along with predictions of possible future increases in global air temperatures have stimulated interest in the effects of [CO 2 ] and temperature on the growth and yield of food crops. This study was conducted to determine the effects and possible interactions of [CO 2 ] and temperature on the growth and yield of rice ( Oryza saliva L., cultivar IR-30). Rice plants were grown for a season in outdoor, naturally sunlit, controlled-environment, and plant growth chambers. Temperature treatments of 28/21/25, 34/27/31, and 40/33/37°C (daytime dry bulb air temperature/night-time dry bulb air temperature/paddy water temperature) were maintained in [CO 2 ] treatments of 330 and 660 μmol CO 2 mol −1 air. In the 40/33/37°C temperature treatment, plants in the 330 μmol mol −1 [CO 2 ] treatment died during stem extension while the [CO 2 ] enriched plants survived but produced sterile panicles. Plants in the 34/27/31°C temperature treatments accumulated biomass and leaf area at a faster rate early in the growing season than plants in the 28/21/25°C temperature treatments. Tillering increased with increasing temperature treatment. Grain yield increases owing to [CO 2 ] enrichment were small and non-significant. This lack of [CO 2 ] response on grain yield was attributed to the generally lower levels of solar irradiance encountered during the late fall and winter when this experiment was conducted. Grain yields were affected much more strongly by temperature than [CO 2 ] treatment. Grain yields declined by an average of approximately 7–8% per 1°C rise in temperature from the 28/21/25 to 34/27/31°C temperature treatment. The reduced grain yields with increasing temperature treatment suggests potential detrimental effects on rice production in some areas if air temperatures increase, especially under conditions of low solar irradiance.

Developmental responses of rice to photoperiod and carbon dioxide concentration

Abstract The documented increase in the carbon dioxide concentration of the Earths atmosphere has stimulated interest in the effects of CO 2 on plants and in particular the future prospects for the worlds food supplies. While rice is a major food crop, relatively little is known about the effects of CO 2 concentration on the timing of physiological growth stages and total growth duration, which are important aspects of a rice cultivars adaptability to the environment of a particular geographic region. The objective of this study was to determine the developmental responses of a modern, improved rice cultivar ( Oryza sativa , cultivar ‘IR-30’) to a range of CO 2 concentrations under two contrasting photoperiods. Rice plants were grown season-long in an outdoor, naturally lit, computer-controlled environment, plant growth chambers in CO 2 , concentrations of 160, 250, (subambient) 330 (ambient), 500, 660 and 900 (superambient) μmol CO 2 mol −1 air. The entire experiment was conducted twice during 1987. The first or early planted rice (EPR) experiment was conducted with photoperiod extension lights during the vegetative phase of development, while the second or late-planted rice (LPR) experiment was conducted using only naturally occurring photoperiod. In both experiments, mainstem leaf developmental rates were greater during vegetative rather than reproductive growth stages and leaf appearance rates increased with CO 2 treatment during vegetative development. In the LPR experiment, panicle initiation and boot stage occurred earlier and total growth duration was shortened for rice plants in the superambient compared with ambient and subambient CO 2 treatments. This acceleration of plant development with increasing CO 2 treatment was associated with a CO 2 -induced decrease in the number of mainstem leaves formed during the vegetative phase of growth. The reduced developmental response of rice plants to CO 2 in the EPR compared with the LPR experiment was attributed to the artificially extended photoperiod during the EPR experiment forcing a delay in the onset of reproductive development particularly in the superambient treatments. The CO 2 -induced acceleration of development and shortening of total growth duration should become a topic of interest for rice agronomists and breeders involved with selecting rice cultivars and agronomic practices for a particular geographic region in view of the continued increases in global atmospheric CO 2 concentration.

RESPONSE OF CROP YIELD TO PREDICTED CHANGES IN CLIMATE AND ATMOSPHERIC CO2 USING SIMULATION

ABSTRACT Soybean growth and yield for 19 locations in southeastern U.S.A. were simulated for 30 years (1951-80) of climate data. Three different climate change scenarios, with and without supplemental irrigation, were used with the SOYGRO crop model. The three climate scenarios were standard historic data and two scenarios based on changes predicted by two general circulation models (GCM) for a doubling of atmospheric carbon dioxide. Results were analyzed for four different conditions; normal weather, doubled CO2 alone, climate change alone, and the combined effect of climate change and doubled CO2. Results indicate 1) yields vary widely with climate scenario; 2) increased water use and irrigation need for the combined case of doubled CO2 and climate change; and 3) simulation is a useful tool for this type of study.

Evaluation of the CROPGRO-Soybean model over a wide range of experiments

Crop simulation models are increasingly being used to predict yield responses to soil, weather, and management conditions. This requires that the models be evaluated for their abilities to accurately respond to those factors. Our objective in this paper was to evaluate the recently released CROPGRO-Soybean model for its ability to simulate soybean growth, seed yield, flowering dates, and season lengths over a wide range of conditions. Inputs (weather, soil characteristics, management practices) and data on growth and yield were assembled for several cultivars from various locations in the USA. The Bragg cultivar was evaluated in multiple years at Gainesville under varying water supply and showed the model ability to predict water limitations on growth. The Williams cultivar was evaluated in multiple years at sites in Iowa, Ohio, and Florida and illustrated CROPGRO ability to predict in different locations and climatic environments. Planting date studies were simulated for three cultivars in North Carolina to evaluate model ability to predict yield response to planting date. Model ability to predict growth and yield in two ‘on-farm’ soybean trials was evaluated. Three of the experiments (Williams cultivar in Florida, planting date trials, and on-farm trials) represent independent data never used in model calibration and illustrate the ability of CROPGRO to predict growth and yield in new locations and environments. We conclude that CROPGRO-Soybean gives reasonable predictions under a wide range of environmental conditions.

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.

Evaluation of the portable chamber technique for measuring canopy gas exchange by crops

An experiment was conducted to evaluate the portable chamber technique for concurrent measurement of canopy evapotranspiration (ET) and carbon exchange rate (CER) and to validate the ET measurements using weighing lysimeters. Hourly gas exchange measurements were made on a half-day diurnal basis over a wide range of leaf area indices (0.2–3.8 m2 m−2) on peanut (Arachis hypogaea L.) for both irrigated and dry soil conditions. The chamber technique used the LI-COR LI-6200 portable photosynthesis system with an open leaf chamber inside the canopy chamber. Measurement of vapor pressure was achieved with the leaf chamber humidity sensor and thermocouple, while carbon dioxide was cycled through the system infra-red gas analyzer. Full-sun and dark CER measurements were made to compute gross photosynthesis (PG). Based on replicated measurements, the average coefficients of variation for PG and ET were 15% and 17%, respectively. The field and lysimeter sites gave comparable values of ET and PG. The PG responses to varying light and LAI conditions were consistent with previously cited values. Computed chamber ET must be extrapolated back to the time the chamber is closed to obviate underestimation of the ET rate (1% s−1) owing to the increase in vapor pressure in the chamber. A correction in ET for reduced light owing to radiation transmission losses appears to be unnecessary. Under varying light conditions where measurements were intentionally made during the sunny periods, the chamber overestimated the instantaneous lysimeter ET. Under clear sky conditions, chamber ET compared well with both instantaneous and hourly lysimeter ET. In the latter comparison, errors were less than 0.13 mm h−1 with and R2 of 0.90. This good agreement was consistent over the morning hours, across the range of leaf area indices, and for both wet and dry soil conditions.