Evaluation of the groundnut model PNUTGRO for crop response to water availability, sowing dates, and seasons

doi:10.1016/0378-4290(94)90017-5

Field Crops Research

Volume 39, Issues 2–3, December 1994, Pages 147-162

https://doi.org/10.1016/0378-4290(94)90017-5 Get rights and content

Abstract

Field experiments were conducted during the period from 1987 to 1992 at four locations in India to collect data to test and validate the groundnut (Arachis hypogea L.) model PNUTGRO for its capability to predict phenology, growth, and yield. Groundnut (cv. Robut 33-1) was grown during the rainy and post-rainy seasons at these sites under various management practices such as sowing dates and differential irrigation. Using the data sets from several years, the model was calibrated for genetic coefficients of cvs. Robut 33-1 and TMV 2 determining their phenology and growth, as well as for soil physical parameters influencing the soil water balance. The model was validated for cv. Robut 33-1 against independent data sets obtained from field experiments conducted during the later years. The model predicted the occurrence of flowering and podding within ±5 days of observed values at locations where growth stages were recorded most frequently. Predictions of growth stages beyond podding were less accurate because of difficulties, associated with the indeterminate nature of the crop, to record growth stages after pod growth has started in the soil. Changes in vegetative growth stages, total dry matter accumulation, growth of pods and seeds, and soil moisture were predicted accurately by the model. Predicted pod yields were significantly correlated (r² = 0.90) with observed yields. These results indicate that under biotic stress-free situations, the model PNUTGRO can be used to predict groundnut yieldsin different environments as determined by season, sowing date, and moisture regimes.

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Growth stages of peanut (Arachis hypogeaea L.)
Peanut Sci.
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K.J. Boote et al.
Applications of, and limitations to, crop growth simulation models to fit crops and cropping systems to semi-arid environments
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Modeling growth and yield of groundnut
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Modeling growth and yield of groundnut
IBSNAT (International Benchmark Soils Network for Agrotechnology Transfer)
Decision Support System for Agrotechnology Transfer (DSSAT), V2.1.

There are more references available in the full text version of this article.

Cited by (43)

Evaluation of the CROPGRO-Peanut model in simulating appropriate sowing date and phosphorus fertilizer application rate for peanut in a subtropical region of eastern India
2017, Crop Journal
Citation Excerpt :
An alternative approach is to use validated crop growth models and historical climatic data to evaluate various crop management strategies for locations or regions on a long-term basis. To meet these objectives, peanut crop models have been developed in the U.S. [10,11,15,17,18] and in India [24] to quantify growth responses to various management practices. The Cropping System Model (CSM)-CROPGRO-Peanut is a process-oriented model that is part of the Decision Support System for Agrotechnology Transfer (DSSAT) [6,12,16].
Projected changes in weather parameters, mainly temperature and rainfall, have already started to show their effect on agricultural production. To cope with the changing scenarios, adoption of appropriate management strategies is of paramount importance. A study was undertaken to evaluate the most appropriate combination of sowing date and phosphorus fertilization level for peanut crops grown in sandy loam soil in a subhumid region of eastern India. Field experiments were conducted during the summer seasons of 2012 and 2013 on peanut crops at the farm of the Indian Institute of Technology, Kharagpur. The DSSAT v4.5 CROPGRO-Peanut model was used to predict the phenology, growth, and yield of peanut crop under combinations of four sowing dates and four phosphorus fertilization levels. The model was calibrated with a 2012 dataset of growth, phenology, and yield parameters for estimating the genetic coefficients of cultivar TMV-2 and was validated with a 2013 dataset of the same parameters. Simulations of pod yield and other yield parameters using the calibrated model were found to be quite accurate. The model was able to reasonably simulate pod yield and final biomass with low normalized root mean square error (RMSE_n), low absolute root mean square error (RMSE_a) and high coefficient of determination (R² > 0.7) over a wide range of sowing dates and different phosphorus fertilization levels sensitivity analysis indicated that sowing from the second week of January to the end of February with 30–50 kg P₂O₅ ha^− 1 would give the highest pod yield.
Adapting and evaluating the CROPGRO-peanut model for response to phosphorus on a sandy-loam soil under semi-arid tropical conditions
2015, Field Crops Research
Citation Excerpt :
The model simulates carbon balance, crop and soil N balance, and soil-crop water balance on a daily basis. The model responds dynamically to daily weather inputs and can simulate effects of cultivar choice, crop and soil management practices such as sowing dates, sowing density, irrigation and fertilizer management on groundnut growth, development and yield (Singh et al., 1994; Dangthaisong et al., 2006; Putto et al., 2009). The CROPGRO-peanut model has been evaluated and tested widely for its capability to simulate soil water, growth and yield (Singh et al., 1994; Naab et al., 2004; Dangthaisong et al., 2006).
Phosphorus (P) deficiency is a major constraint to crop production in many agricultural systems globally. Application of phosphorus fertilizer is essential for optimal crop yields when soils are P limiting. Crop simulation models can provide an alternative, less time consuming and inexpensive means of determining the optimum crop P requirements under varied soil and climatic conditions. The CROPGRO-peanut model is capable of simulating the growth and yield of peanut in response to weather, soil, water, nitrogen and management practices but its capability in predicting crop responses to soil and fertilizer P needs to be established. Our objective was to adapt and evaluate the CROPGRO-peanut model within the DSSAT system to simulate the growth and yield of peanut (Arachis hypogaea) in response to soil and fertilizer P. Data from four P fertilizer treatments (0, 13, 26 and 39 kg P ha⁻¹) and two cropping seasons (2002 and 2003) experiments on an Alfisol were used to calibrate the model. The model was tested using two data sets from a P fertilizer × cultivar trial conducted on-station in 1997 and 1998 and P fertilizer × fungicide trials on-farm in 2002. The limited testing showed that the P module accurately simulated the seasonal patterns of aboveground biomass and pod yield in the on-farm trials. Averaged across sampling dates RMSEs in the on-farm trials ranged from 100 to 398 kg ha⁻¹ (d ≥ 0.99) for total biomass and from 97 to 263 kg ha⁻¹ (d values ≥ 0.98) for pod yield. At final harvest, the variability of simulated biomass and pod yield was about 5.4 and 10.0% of the observed biomass and pod yield respectively. In the P fertilizer × cultivar trial, the variability of simulated biomass and pod yield were 22 and 13% for cv. F-Mix and 19 and 5% for cv. Chinese respectively. The model simulated the seasonal patterns of vegetative P content in the four farmers’ fields fairly well with RMSEs ranging from 0.28 to 1.29 kg ha⁻¹ when averaged across measurements dates. The model outputs were sensitive to soil P test values, the method of P fertilizer application and to plant P uptake factors, such as root P extraction radius and root length density. There were differences in the sensitivity of the simulations to changes in target tissue P concentrations. Increasing or decreasing the optimum P concentration of seed and minimum P concentrations of leaf and stem had the greatest effects on pod yield compared to other plant parts. We conclude that the generic soil and plant P model in DSSAT 4.5 is capable of simulating peanut growth and yield in response to soil P levels or fertilizer application on an Alfisol.
Effect of Climate Change Factors on Processes of Crop Growth and Development and Yield of Groundnut (Arachis hypogaea L.)
2012, Advances in Agronomy
Citation Excerpt :
Elevated CO2 does not affect vegetative progression of groundnut (Rao, 1999). Currently in the groundnut model (Boote et al., 1986, 1991, 1998; Singh et al., 1994a,b), the base temperature is 11 °C and the OTs for vegetative development range from 28 to 30 °C, and the damaging threshold temperature is taken as 55 °C. There is little information in the literature on how vegetative development is affected by temperatures above 30 °C.
Global warming is changing climate in terms of increased frequency of extreme weather events as well as increased air temperature and vapor pressure deficit of air and spatial and temporal change in rainfall. In spite of the beneficial effect of increased atmospheric CO₂ concentration, climate change will adversely impact the production and productivity of groundnut grown in subtropical and tropical regions of the world. This chapter reviews the current state of knowledge on effects of climate change factors on the growth and development of groundnut. This review identifies research gaps and suggests upgrades to groundnut models, such as the CROPGRO-Groundnut model, which is being used as a tool to assess impacts of climate change on groundnut crop. This review revealed that the direct and indirect effects of most climate change factors on plant growth and development processes are well understood and already incorporated in the CROPGRO-Groundnut model. Extreme events associated with climate change may sometimes cause water-logging, extreme soil water deficiency, or extreme humidity conditions, and these effects could be better addressed in the models.
Multi-environment evaluation of peanut lines by model simulation with the cultivar coefficients derived from a reduced set of observed field data
2009, Field Crops Research
A major limitation of the application of a crop simulation model is the determination of cultivar coefficients, as the recommended procedure requires extensive data sampling throughout the growing season which is very impractical when a large number of lines are involved or when critical resources are limited. Our previous study has shown that a reduced set of experimental data can be used to accurately estimate the cultivar coefficients of peanut lines as used by the CSM-CROPGRO-Peanut model. The objectives of this study were to verify our previous finding and to evaluate the derived cultivar coefficients in assisting multi-environment evaluation of peanut lines with the CSM-CROPGRO-Peanut model. Nine peanut lines in a regional yield trial (Set I) and ten peanut lines in a standard yield trial (Set II) were grown during the dry and rainy seasons of 2005. Data were collected on plant growth and development following the optimum protocol from our previous study. These data were used for model calibration to derive the cultivar coefficients of the individual peanut lines. Model calibration showed simulated values of phenology and growth characteristics of the peanut lines that were close to the corresponding observed values, with the coefficient of determination (r²) and the index of agreement (d) close to optimal values of 1, and a normalized root mean square error (RMSE_n) smaller than 35%. Genetic variation among lines in cultivar coefficients was also observed. The initial model evaluation with data collected in the 2004 rainy season confirmed that model prediction was good for independent data, i.e., giving high values of r² and d; and small RMSE_n. The derived cultivar coefficients were shown to enable the CSM-CROPGRO-Peanut model to satisfactorily mimic yield ranking and stability of peanut lines in the Set I and Set II yield trials with 10 and 8 environments, respectively. Among the top five highest yielding lines based on mean observed pod yield (upper 56% for the Set I yield trial and upper 50% for the Set II yield trial), four lines were identified by model simulation in both sets. Also, the same top yielding lines in the two sets were identified by both simulation and experimentation. The model predicted similar GGE biplot patterns as present in observed trials, and also identified the same stable lines as the observed data. It is concluded that a reduced set of field data can be used for model application in assisting the multi-environment evaluation of peanut lines.
Analysis of potential yields and yield gaps of rainfed soybean in India using CROPGRO-Soybean model
2008, Agricultural and Forest Meteorology
To assess the scope for enhancing productivity of soybean (Glycine max L. Merr.), the CROPGRO-Soybean model was calibrated and validated for the diverse soybean-growing environments of central and peninsular India. The validated model was used to estimate potential yields (water non-limiting and water limiting) and yield gaps of soybean for 21 locations representing major soybean regions of India. The average water non-limiting potential yield of soybean for the locations was 3020 kg ha⁻¹, while the water limiting potential was 2170 kg ha⁻¹ indicating a 28% reduction in yield due to adverse soil moisture conditions. As against this, the actual yields of locations averaged 1000 kg ha⁻¹, which was 2020 and 1170 kg ha⁻¹ less than the water non-limiting potential and water limiting potential yields, respectively. Across locations the water non-limiting potential yields were less variable than water limited potential and actual yields, and strongly correlated with solar radiation during the season (R² = 0.83, p ≤ 0.01). Both simulated water limiting potential yield (R² = 0.59, p ≤ 0.01) and actual yield (R² = 0.33, p ≤ 0.05) had significant but positive and curvilinear relationships with crop season rainfall across locations. The gap between water non-limiting and water limiting potential yields was very large at locations with low crop season rainfall and narrowed down at locations with increasing quantity of crop season rainfall. On the other hand, the gap between water limiting potential yield and actual farmers yield was narrow at locations with low crop season rainfall and increased considerably at locations with increasing amounts of rainfall. This yield gap, which reflects the actual yield gap in rainfed environment, is essentially due to non-adoption of improved crop management practices and could be reduced if proper interventions are made. The simulation study suggested that conservation of rainfall and drought resistant varieties in low rainfall regimes; and alleviation of water-logging and use of water-logging tolerant varieties in high rainfall regimes will be the essential components of improved technologies aimed at reducing the yield gaps of soybean. Harvesting of excess rainfall during the season and its subsequent use as supplemental irrigation would further help in increasing crop yields at most locations.
Regional importance of crop yield constraints: Linking simulation models and geostatistics to interpret spatial patterns
2006, Ecological Modelling
Citation Excerpt :
To evaluate the impact on the yield gap of changes in environmental conditions, such as those that might be achieved through policy instruments, an understanding of what drives variability in y(u) is needed. Typically, this means obtaining observations of e(u) in farmers fields, which can then be combined with pre-defined models of f (e.g., Aggarwal and Kalra, 1994; Singh et al., 1994; Matthews et al., 2002) or with statistical models relying on joint observations of y(u) to determine the contribution of different factors to yield variability (Calvino and Sadras, 2002; Lobell et al., 2004). While these studies have provided new insights into the yield gap in specific regions, detailed understanding of the causes and often even the magnitudes of yield gaps are poorly known in many regions.
Over the next few decades, a central goal of agricultural research and policy will be to increase average regional crop yields in the face of diminished gains in genetic yield potential, likely climatic changes, decreased resource availability, and stricter environmental standards. Fundamental to the pursuit of effective investment strategies is an ability to quantify tradeoffs associated with potential policy and management changes. However, the data needed to predict regional yield responses to change, namely observations of yields and climatic, soil, and management conditions in farmers’ fields, are often difficult to obtain. In this paper, we investigate the value of data on the spatial distribution of yields for understanding causes of landscape yield variability. Stochastic simulation models, which employ the CERES model to simulate crop yields across a landscape, are used to translate assumed spatial patterns of soil and management conditions into spatial pattern of yields. Monte Carlo simulation is then used to repeat this process for many different realizations of conditions, resulting in a modeled relationship between yield patterns and the relative importance of soil and management yield constraints, both of which can be computed in the controlled simulation environment. The derived relationship then allows one to infer from observed yield patterns the true proportion of yield variability explained by soil and management.
This procedure was tested for wheat in the Yaqui Valley, an intensive agricultural region in Sonora, Mexico, where yield patterns have been previously estimated with remote sensing. Comparison of simulated and observed yield patterns indicated that roughly 80% of spatial yield variance in 2001–2002 was attributable to management variations. The ability of simulation models to aid interpretation of landscape patterns is potentially invaluable for understanding yield constraints in many agricultural regions, where direct observations of soil and management variables are infeasible.

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Field Crops Research

Abstract

References (15)

Growth stages of peanut (Arachis hypogeaea L.)

Peanut Sci.

Applications of, and limitations to, crop growth simulation models to fit crops and cropping systems to semi-arid environments

Modeling growth and yield of groundnut

PNUTGRO v1.0, Peanut crop growth and yield model. Technical documentation

PNUTGRO V1.02, Peanut crop growth simulation model. User's Guide

Modeling growth and yield of groundnut

Decision Support System for Agrotechnology Transfer (DSSAT), V2.1.

Cited by (43)

Evaluation of the CROPGRO-Peanut model in simulating appropriate sowing date and phosphorus fertilizer application rate for peanut in a subtropical region of eastern India

Adapting and evaluating the CROPGRO-peanut model for response to phosphorus on a sandy-loam soil under semi-arid tropical conditions

Effect of Climate Change Factors on Processes of Crop Growth and Development and Yield of Groundnut (Arachis hypogaea L.)

Multi-environment evaluation of peanut lines by model simulation with the cultivar coefficients derived from a reduced set of observed field data

Analysis of potential yields and yield gaps of rainfed soybean in India using CROPGRO-Soybean model

Regional importance of crop yield constraints: Linking simulation models and geostatistics to interpret spatial patterns