Application of the GOSSYM/COMAX system to cotton crop management

McKinion, James M.; Baker, Dana Lee; Whisler, F. D.; Lambert, J. R.

doi:10.1016/0308-521x(89)90012-7

Cited by 90 publications

(35 citation statements)

References 8 publications

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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).…”

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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“…The daily amount of dry matter partitioned to roots (DBM root , g m À2 per day) is calculated from a simplified equation obtained using a more complex model of cotton (GOSSYM; Mckinion et al, 1989). A unique relationship between DBM root and shoot growth rate (DBM shoot , g m À2 per day) was obtained for both rainfed and irrigated conditions at Córdoba running GOSSYM, finding:…”

Section: Root-growth Modelmentioning

confidence: 99%

Modeling root growth and the soil–plant–atmosphere continuum of cotton crops

Coelho

Villalobos

Mateos

2003

Agricultural Water Management

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A simple and functional model to simulate cotton root growth is coupled to models of water balance and leaf water potential to predict the behavior of the soil-plant-atmosphere continuum (SPAC) of cotton crops. The root-growth model takes into account the dry matter partitioning to roots, root depth increase as a function of thermal time, soil mechanical resistance and soil water stress, and allows the inclusion of spatial variability of soil physical properties. The water balance model uses a cascade approach to simulate drainage while soil and plant evaporation are calculated separately following the model of Ritchie. The leaf water potential model (LWPM) is based on Ohm's Law analogy, where water flow resistance to the roots is calculated using an empirical equation and the resistance within the plant is calculated as a function of a threshold leaf water potential. The model simulated correctly the soil resistance and the temporal and spatial distribution of roots in the soil profile. The model simulated total soil water content and midday leaf water potential well, even when a compacted soil layer was present, but the agreement to observed data was poor for the vertical distribution of soil water content, mainly in the zone of greatest root concentration. #

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“…The most common growth models used in application for cotton are the GOSSYM (Mckinion et al, 1989) and COTONS models. On the same analogy the PNUTGRO (Boote et al, 1989) for groundnut, CHIKPGRO for chick pea, WTGROWS for wheat, SOYGRO for soybean, BEANGRO (Hogenboom et al, 1994) for beans QSUN for sunflower are in use simulating crop growth on spatial domain.…”

Section: Applications Of Crop Simulation Modelsmentioning

confidence: 99%

“…On farm applications of models were also reported (e.g. Lindemann et al, 1987;McKinion et al, 1988). Models such as SUCROS and others associated with the 'School of de Wit' (Bouman et al, 1996) as well as those of the CERES (Ritchie et al, 1998) and CROPGRO (Boote et al, 1998) families of models had a significant impact on the crop modeling community.…”

Section: Introductionmentioning

confidence: 97%

Remote Sensing, GIS and Crop Simulation Models – A Review

Nagamani¹,

Mariappan²

2017

Int. J. Curr. Res. Biosci. Plant Biol.

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A b s t r a c t A r t i c l e I n f oAgriculture continues to be the backbone of Third World economies. In India, more than two-thirds of population depends on agriculture. Agriculture provides the principal means of livelihood for over 58.4% of India's population. So the promotion of agriculture is an integral part of developmental programmes. The advances through information technology and space technology need to be extended to agriculture as well. Agriculture is always vulnerable, because of unfavorable weather and climatic conditions. So, it needs constant monitoring to improve crop productivity. The linkages among crop varieties, irrigation, soil characteristics, weather, etc., which are the key factors in agricultural productivity can be effectively made with the help of Remote Sensing and GIS tools. Crop Simulation Model (CSM) is a valuable tool to researchers to help them to understand the influence of climatic variables on crop productivity. Simulation models also provide global edge to the farmers and researchers since they are objective, fast and cost effective. The scope of applicability of these simulation models can be extended too much broader scales for regional planning and policy analysis by combining their capabilities with a Geographic Information System (GIS). The principle of this study appeal to review on Remote Sensing (RS), GIS and CSM, and on types of models and its limitations. Overview of CSM models in the current scenario, as well utilization of RS and GIS tools that model the impacts of agricultural interventions.

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Application of the GOSSYM/COMAX system to cotton crop management

Cited by 90 publications

References 8 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

Modeling root growth and the soil–plant–atmosphere continuum of cotton crops

Remote Sensing, GIS and Crop Simulation Models – A Review

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