Adapting the CROPGRO Model to Predict Growth and Perennial Nature of Bahiagrass

The objective of this research was to modify an existing crop growth model for ability to predict growth and composition of bahiagrass (Paspalm notatum Flügge) in response to daily weather and management inputs. The CROPGRO–CSM cropping systems model has a generic, process-oriented structure that allows inclusion of new species and simulating cropping sequences and crop rotations. An early adaptation of CROPGRO-CSM “species files” for bahiagrass over-predicted growth during late fall through early spring, and totally failed in re-growth if all foliage was lost from freeze damage. Revised species parameters and use of “pest damage” offered only a partial solution. Three processes, absent from the annual CROPGRO-CSM model, contributed to prediction of excessive cool-season growth: (1) no provision for storage (reserve) structures, (2) lack of winter dormancy, and 3) freeze damage killed all leaves at once and resulted in crop death. In addition, the model lacked the CO2-concentrating effect of C4 photosynthesis in the leaf photosynthesis routines. Therefore, we modified the source code of CROPGRO to include these processes to improve biological accuracy of re-growth patterns and prediction of seasonal patterns of growth (Rymph et al., 2004)

Modeling the regrowth of forage grasses: simulating growth, partitioning, and carbon and nitrogen metabolism.

Reserves play an important role in plants undergoing stress. Plants adapted to defoliation use reserve compounds to regrow leaf area. Modeling grass regrowth should account for these processes. A field experiment was conducted in Gainesville, FL, to study herbage production, partitioning and mobilization of reserve compounds of two tropical grasses (Jiggs bermudagrass and Mulato-2 brachiaria grass), under the combination of two light levels ? 56% and 100% solar radiation, and two N rates ? 30 and 120 kg N ha-1 after each harvest. Herbage mass was quantified at harvest every 28 days.Resumo 379-7

Modeling growth and yield of groundnut

Crop simulation models have much potential for assisting in agrotechnology transfer, crop management decision-making, climatic assessment, and in the synthesis of research results. For these reasons, it is important to continue to develop and improve models for predicting the growth and yield of groundnut (Arachis hypogaea). In this paper, we briefly review approaches for modeling growth and yield of groundnut. Then we illustrate major areas of improvement in the PNUTGRO crop growth model after evaluating PNUTGRO Vi.02 versus additional data sets from Florida and India. New areas of improvement include: 1) addition of a hedgerow photosynthesis submodel to improve response to row spacing, sowing density, and growth habit; 2) addition of the Pen mall equation to incorporate vapor pressure deficit and windspeed to estimate evapotranspiration for arid regions; 3) modification oJfunctions for prediction of crop del'elopment; and 4) modification of the effects of stress environments such as high temperature and vapor pressure deficit on partitioning

The CROPGRO Perennial Forage Model Simulates Productivity and Re-Growth of Tropical Perennial Grasses

This paper introduces the CROPGRO Perennial Forage model (CROPGRO-PFM) and describes its ability to simulate regrowth dynamics and herbage production of Brachiaria and Panicum as affected by harvest management and weather. The model simulates regrowth, herbage harvests, percent leaf, and herbage protein of perennial forage grasses and legumes over multiple seasons. It can regrow from zero LAI (after harvest) based on use of carbohydrate and N reserves in storage tissues; however, the amount of residual stubble and residual leaf area index (LAI) are also important for rapid regrowth and productivity. The model is publically available for download from DSSAT.NET

Evaluation of the groundnut model PNUTGRO for crop response to plant population and row spacing

Field experiments were conducted during the 1987, 1991 and 1992 rainy seasons at Patancheru, Andhra Pradesh, to collect data to test and validate the hedgerow version of the groundnut model PNUTGRO for predicting phenological development, light interception, canopy growth, DM production, pod and seed yields of groundnuts as influenced by row spacing and plant population. The model was calibrated using the crop growth and phenology data of groundnut cv. Robut 33-1 obtained from the 1987 and 1991 rainy season experiments. Groundnuts were grown at plant populations ranging from 5 to 45 plants/sq m with and without irrigation. Changes were made in the cultivar-specific coefficients related to the light penetration into the crop canopy and DM production. The model was validated against independent data obtained from a 1992 rainy season experiment. In 1992, groundnuts were grown at plant populations ranging from 10 to 40 plants/sq m and at row spacings of 20, 30 and 60 cm. The model predicted the occurrence of vegetative and reproductive stages, canopy development, total DM production and its partitioning to pods and seed accurately. Maximum LAI observed during the season was significantly correlated with simulated values (sq r = 0.95). In spite of some incidence of diseases and pests, the correlation between simulated and observed pod yield was significant (sq r = 0.61). It is concluded that the hedgerow version of the groundnut model PNUTGRO can be used to quantify groundnut growth and yields as influenced by plant population and row spacin

Shade and nitrogen effects on regrowth dynamics, partitioning, and herbage production of Jiggs Bermudagrass and Mulato-2 brachiaria hybrid.

The objectives of this study were to quantify the effects of shade and N fertilization on C and N availability for plant growth, composition of stubble following defoliation, and partitioning of assimilates.Resumo 307-38

Using the CROPGRO-peanut model to quantify yield gaps of peanut in the Guinean Savanna Zone of Ghana

Peanut (Arachis hypogaea L.) yield in Ghana is limited by variable rainfall, low soil fertility, pests and diseases, and poor crop management. Field experiments were conducted during the 1997 and 1998 seasons at the Savanna Agricultural Research Station in Ghana to evaluate the CROPGRO-peanut model for its ability to simulate growth, yield, and soil water balance of a peanut crop and to quantify yield losses caused by biotic and abiotic factors. Two peanut cultivars, Chinese which matures in 90 d, and F-Mix which matures in 120 d, were grown rainfed on an Alfisol soil at three sowing dates between May and August in 1997 and at four dates in 1998. Soil water and crop growth were measured during the season and compared with crop model simulations to determine yield-limiting factors relative to potential yield. Growth and yield were highest for the early sowing dates and decreased progressively with later sowing, a trend attributed to leaf diseases. After incorporating functions for percentage leaf defoliation and percentage diseased leaf area, the model accurately simulated soil water content fluctuations, crop growth, and yield of cultivars for the sowing dates and seasons. Simulated yield losses caused by water deficits were small (averaging 5–10%) for early sowing dates (late May to mid-July) and increased with later sowing dates (20 and 70% for third and fourth sowings). Yield losses due to diseases and pests were simulated as a percentage of potential yield under water-limited environments and averaged 40%, also increasing with later sowing dates. Using 13 yr of weather data, simulated yields were reduced 10 to 20% by water deficit for the two earlier (normal) sowing dates, but more for the later sowing dates, while additional yield reductions were attributed to biotic stresses. We conclude that the CROPGRO-peanut model can be successfully used to quantify the yield potential and yield gaps associated with yield-reducing stresses and crop management for this region

Climate Impacts on Agriculture: Implications for Crop Production

Changes in temperature, CO2, and precipitation under the scenarios of climate change for the next 30 yr present a challenge to crop production. This review focuses on the impact of temperature, CO2, and ozone on agronomic crops and the implications for crop production. Understanding these implications for agricultural crops is critical for developing cropping systems resilient to stresses induced by climate change. There is variation among crops in their response to CO2, temperature, and precipitation changes and, with the regional differences in predicted climate, a situation is created in which the responses will be further complicated. For example, the temperature effects on soybean [Glycine max (L.) Merr.] could potentially cause yield reductions of 2.4% in the South but an increase of 1.7% in the Midwest. The frequency of years when temperatures exceed thresholds for damage during critical growth stages is likely to increase for some crops and regions. The increase in CO2 contributes significantly to enhanced plant growth and improved water use efficiency (WUE); however, there may be a downscaling of these positive impacts due to higher temperatures plants will experience during their growth cycle. A challenge is to understand the interactions of the changing climatic parameters because of the interactions among temperature, CO2, and precipitation on plant growth and development and also on the biotic stresses of weeds, insects, and diseases. Agronomists will have to consider the variations in temperature and precipitation as part of the production system if they are to ensure the food security required by an ever increasing population