Calibration of GOSSYM: Theory and practice

Boone, M. Y. L.; Porter, Darrell; McKinion, James M.

doi:10.1016/0168-1699(93)90038-3

Cited by 25 publications

(16 citation statements)

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“…However, most of the cotton crop models used in literature (McKinion et al, 1989 for the GOSSYM/COMAX model; Jallas et al, 1999 for the COTONS model) require a large number of parameters to describe the crop environment (soil hydrologic characteristics, soil carbon and nitrogen contents and daily climatic variables) and management (cultivar, dates and characteristics of cultural practices). For instance, the variety file of the GOSSYM/COMAX model contains 50 parameters that modify the growth and development of cotton cultivars (Boone et al, 1993). Owing to this large number of parameters, such models are difficult to calibrate and their results difficult to extrapolate from one site to another.…”

Section: Cotton Water Requirement Satisfaction Indexmentioning

confidence: 99%

Multi‐scales and multi‐sites analyses of the role of rainfall in cotton yields in West Africa

Sultan

Bella-Medjo

Berg

et al. 2009

Intl Journal of Climatology

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ABSTRACT:Cotton is the main tradable crop of West and Central African countries, representing for some countries the main exported agricultural product. Cotton is then of major concern since it represents an important source of income, accounting for more than a tenth of total exports. Moreover, the subsector as a whole is essential for rural poverty reduction. Since cotton is a rainfed crop in these countries, its yield is closely related to climate, in particular to rainfall variability.The objective of this study is to point out the role of rainfall variability in cotton yields. Our approach consists in taking two completely different sites in the analysis of the climate-yields relationships, i.e. an experimental plot in Mali with a long-term historical yield-survey and farmers' yields in 28 administrative units in Cameroon. We found that the same rainfall parameters (rainy season onset and length) are major drivers for the year-to-year and the spatial distribution of cotton productivity, even if the role of rainfall variability is strongly reduced in farmers' exploitations where other non-climatic factors such as human management, biotic stresses, pests, etc., impact crop productivity. The link between rainfall and cotton yields seems to depend on the mean climate since the driest cotton areas in Cameroon are the most sensitive to climate variability. The coherence of the results from the two very different situations gives us some confidence in the generalization of our findings to the whole West and Central Africa. Our study shows also that the aggregation of yield data from the local scale to the national scale tends to smooth the non-climatic variability and highlight the role of climate in the year-to-year variability of cotton yields. These results are important in working towards the predictability of crop yields using rainfall information, in particular to highlight the most salient rainfall parameters that are needed in a forecast system.

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Section: Cotton Water Requirement Satisfaction Indexmentioning

confidence: 99%

Multi‐scales and multi‐sites analyses of the role of rainfall in cotton yields in West Africa

Sultan

Bella-Medjo

Berg

et al. 2009

Intl Journal of Climatology

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“…Its application and resulting credibility across a broad region with geographically distributed grids have yet to be established. Given the driving weather or climatic conditions, the original GOSSYM has approximately an additional 85 tunable parameters for users to calibrate at a farm site (Boone et al, 1993). Specification for such a long list of unknown parameters is extremely difficult to accomplish and constitutes a major obstacle that introduces large uncertainties to the spatially distributed modeling of climate–crop interactions.…”

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confidence: 99%

A Distributed Cotton Growth Model Developed from GOSSYM and Its Parameter Determination

Liang

Gao

et al. 2012

Agronomy Journal

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Prediction of cotton (Gossypium hirsutum L.) production under a changing climate requires a coupled modeling system that represents climate–cotton interactions. The existing cotton growth model GOSSYM has drawbacks that prohibit its effective coupling with climate models. We developed a geographically distributed cotton growth model from the original GOSSYM and optimized it for coupling with the regional Climate–Weather Research Forecasting model (CWRF). This included software redesign, physics improvement, and parameter specification for consistent coupling of CWRF and GOSSYM. Through incorporation of the best available physical representations and observational estimates, the long list of inputs in the original GOSSYM was reduced to two parameters, the initial NO3 amount in the top 2 m of soil and the ratio of irrigated water amount to potential evapotranspiration. The geographic distributions of these two parameters are determined by optimization that minimizes model errors in simulating cotton yields. The result shows that the redeveloped GOSSYM realistically reproduces the geographic distribution of mean cotton yields in 30‐km grids, within ±10% of observations across most of the U.S. Cotton Belt, whereas the original GOSSYM overestimated yields by 27 to 135% at the state level and 92% overall. Both models produced interannual yield variability with comparable magnitude; however, the temporal correspondence between modeled and observed interannual anomalies was much more realistic in the redeveloped than the original GOSSYM because significant (P = 0.05) correlations were identified in 87 and 40% of harvest grids, respectively. The redeveloped GOSSYM provides a starting point for additional improvements and applications of the coupled CWRF–GOSSYM system to study climate–cotton interactions.

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“…The redeveloped, geographically distributed GOSSYM, at the CWRF 30‐km grid spacing, is integrated continuously for the entire growing season (with the longest span from 30 April to 12 November in New Mexico) during 1979 to 2005 under realistic climate conditions and agricultural practices. The original GOSSYM has 55 parameters for users to calibrate information specific to the cotton cultivar at a farm site (Boone et al, 1993; Reddy et al, 2003) and an additional 30 parameters to be specified as input for soil conditions and management practices. To facilitate the spatially distributed modeling of climate–crop interactions, Liang et al (2012a) first minimized the input parameter list by replacing most of these quantities with the best available physical representations and observational estimates.…”

Section: Methodsmentioning

confidence: 99%

Physical Modeling of U.S. Cotton Yields and Climate Stresses during 1979 to 2005

Liang

Gao

et al. 2012

Agronomy Journal

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Climate variability and changes affect crop yields by causing climatic stresses during various stages of the plant life cycle. A crop growth model must be able to capture the observed relationships between crop yields and climate stresses before its credible use as a prediction tool. This study evaluated the ability of the geographically distributed cotton growth model redeveloped from GOSSYM in simulating U.S. cotton (Gossypium hirsutum L.) yields and their responses to climate stresses during 1979 to 2005. Driven by realistic climate conditions, the model reproduced long‐term mean cotton yields within ±10% of observations at the 30‐km model resolution across virtually the entire U.S. Cotton Belt and correctly captured the critical dependence of their geographic distributions on regional climate characteristics. Significant correlations between simulated and observed interannual variations were found across 87% of the total harvest grids. The model also faithfully represented the predictive role of July to August air temperature and August to September soil temperature anomalies on interannual cotton yield changes on unirrigated lands, with a similar but weaker predictive signal for irrigated lands as observed. The modeled cotton yields exhibited large, positive correlations with July to August leaf area index. These results indicate the model's ability to depict the regional impact of climate stresses on cotton yields and suggest the potential predictive value of satellite retrievals. They also provide a baseline reference for further model improvements and applications in the future study of climate–cotton interactions.

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Calibration of GOSSYM: Theory and practice

Cited by 25 publications

References 0 publications

Multi‐scales and multi‐sites analyses of the role of rainfall in cotton yields in West Africa

Multi‐scales and multi‐sites analyses of the role of rainfall in cotton yields in West Africa

A Distributed Cotton Growth Model Developed from GOSSYM and Its Parameter Determination

Physical Modeling of U.S. Cotton Yields and Climate Stresses during 1979 to 2005

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