Validation of the Soil Temperature Subroutine HEAT in the Cotton Simulation Model GOSSYM

Khorsandi, Farhad; Boone, M. Y. L.; Weerakkody, Govinda J.; Whisler, F. D.

doi:10.2134/agronj1997.00021962008900030008x

Cited by 6 publications

(4 citation statements)

References 5 publications

(9 reference statements)

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“…Extensive validation against field measurements indicates that the CSSP realistically simulates observed surface energy and mass variations (Yuan and Liang, 2011a). In contrast, the respective GOSSYM components are much less comprehensive, where the model formulations are generally empirical and especially lack full interactions with atmospheric dynamics that limit model performance (e.g., Staggenborg et al, 1996; Khorsandi et al, 1997). Given the crucial controls that soil temperature and moisture and evapotranspiration have on crop growth, development, and yield, we eliminated the corresponding modules from GOSSYM and, instead, utilized the more realistic prediction provided by the CSSP.…”

Section: Methodsmentioning

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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Section: Methodsmentioning

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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“…Optimal accuracy of the yield estimation model will occur as the slope reaches a value of one, implying that for each observed yield unit will correspond an estimated yield unit. Other models have been validated using this technique (Fritz et al, 1997;Khorsandi et al, 1997).…”

Section: Discussionmentioning

confidence: 99%

An Empirical Model to Predict Yield of Rainfed Dry Bean With Multi-Year Data

Amador-Ramírez

Acosta-Díaz²,

Medina-García

et al. 2007

RevFitotecMex

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The prediction of crop yield and harvest volume of about 700 thousand ha planted to dry bean in Zacatecas State will enable the implementation of strategies to decrease the degree of uncertainty of decisions pertaining to agriculture. The purpose of the present study was to predict bean yield under rainfed conditions using leaf area index (LAI), light interception (LI) by the canopy, and rainfall. LAI and LI of both black-grain and light-colored grain beans were determined at the beginning of flowering, at pod formation, at the beginning of pod filling, and at intermediate pod filling. The relationship yield: LAI/LI/rainfall as well as the verification of a model were examined by linear least-square regression. Maximal LI and its LAI for the various years were 70 % and 1.6 for 2002 and 75 % and 2.5 in 2003. For these years, LI as a function of LAI could be described by an exponential model. LAI and LI at pod formation and the beginning of pod filling were the phenological stages that better explained bean yield for all varieties. The empirical model relating bean yield: LAI/LI/rainfall accounted for 71 % of the variability of light-colored grain bean yield. The corresponding percentages of the variability in measured yields for black-grain beans were 68 % for Emiliano Zapata and Progreso and 74 % for Zaragoza and Miguel Auza. Even though the relationship LAI/LI/rainfall was affected due to the low plant population density, the many varieties employed, and the agroecological sites, the information from this kind of studies will be useful to decision makers and farmers to make decisions.

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“…Because GOSSYM and GLYCIM are mechanistic models, they require comprehensive and reliable information about several variables, including (i) crop variety, (ii) soil characteristics, and (iii) daily weather conditions. Information on crop variety and daily weather conditions are relatively easy to acquire whereas data sets about soil characteristics are harder to acquire and include important soil hydrologic properties, such as soil moisture retention curve, K s , soil bulk density, and texture of each horizon to a 1‐m depth (Khorsandi et al, 1997).…”

Section: Methodsmentioning

confidence: 99%

Soil Physical Properties Web Database for GOSSYM and GLYCIM Crop Simulation Models

et al. 2004

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information (Caldeira and Pinto, 1998), especially mechanistic types of models like GOSSYM and GLYCIM. Knowledge of soil physical properties is needed for various kinds of In general, both dynamic and static types of data are environmental studies, including crop simulation where the intended users are agronomists, consultants, and growers. However, acquiring required to run a crop model. A dynamic data set inand tabulating a complete set of soils analysis data for a particular cludes information about weather (minimum/maximum region is expensive and laborious. This paper describes the constructemperature, solar radiation, precipitation, and wind tion of a web-based soil physical properties database to meet data run), cultural practices (type and rate of fertilizer and requirements of users of cotton (Gossypium hirsutum L.) and soybean herbicides; date and amount of irrigation), initial soil [Glycine max (L.) Merr.] crop simulation models (GOSSYM and NO 3 , percentage organic matter, and initial soil water GLYCIM, respectively) in addition to providing a generic data file content. A static data set includes information on crop of soil physical properties. The data are comprised of undisturbed variety and soil physical properties like soil bulk density, samples of 1074 soil horizons (or about 300 sample sites) collected saturated hydraulic conductivity (K s ), soil moisture refrom farmers' fields using a tractor-mounted hydraulic probe. Stantention curve, and soil texture. Soil physical properties dard laboratory analyses were performed to determine the various soil physical properties. An Oracle8 relational database managementare generally treated as static properties unless fields system was designed and implemented to store and deliver the soil are subjected to severe soil erosion, soil deposition, or physical properties using a client/server approach. A Perl database aggressive land-leveling processes, and databases coninterface was used for the server-side database connectivity to build taining these data have been reported in the literature the soil files. The database accepts queries on several attributes, in-

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Validation of the Soil Temperature Subroutine HEAT in the Cotton Simulation Model GOSSYM

Cited by 6 publications

References 5 publications

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

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

An Empirical Model to Predict Yield of Rainfed Dry Bean With Multi-Year Data

Soil Physical Properties Web Database for GOSSYM and GLYCIM Crop Simulation Models

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