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Title: EVALUATION OF GPFARM FOR DRYLAND CROPPING SYSTEMS IN EASTERN COLORADO

Author
item Andales, Allan
item Ahuja, Lajpat
item PETERSON, GARY - COLORADO STATE UNIVERSITY

Submitted to: Agronomy Journal
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 5/30/2003
Publication Date: 11/1/2003
Citation: Andales, A.A., Ahuja, L.R., Peterson, G.A. 2003. Evaluation of gpfarm for dryland cropping systems in eastern colorado. Agronomy Journal. Vol. 95, pp. 1510-1524.

Interpretive Summary: GPFARM is an ARS decision support system for strategic (long-term) planning. This study evaluated its performance for comparing alternative dryland no-till cropping systems and established limits of accuracy for eastern Colorado, using data collected in 1987 through 1999 from an on-going long-term experiment at three locations along a gradient of potential evapotranspiration (PET) [Sterling, low PET; Stratton, medium PET; and Walsh, high PET]. The crop rotations, which included winter wheat (Triticum aestivum L.), corn (Zea mays L.), sorghum [Sorghum bicolor (L.) Moench], proso millet (M) (Panicum miliaceum L.), and varying fallow periods, were: wheat-fallow, wheat-corn-fallow, and wheat-corn-millet-fallow at Sterling and Stratton; and wheat-fallow, wheat-sorghum-fallow, and wheat-sorghum-millet-fallow at Walsh. The ranges of relative error (RE) of simulated mean and root mean square error (RMSE) were: total soil profile water content (RE: 0 to 23%; RMSE: 38 to76 mm water); dry mass grain yield (RE: -27 to 84%; RMSE: 419 to 2567 kg ha-1); dry mass crop residue (RE: -5 to 42%; RMSE: 859 to 1845 kg ha-1); and total soil profile residual nitrate-N (RE: -42 to 32%; RMSE: 26 to 78 kg ha-1). GPFARM simulations agreed with observed trends and showed that productivity and water use efficiency increased with cropping intensification and that Stratton was the most productive and Walsh the least. GPFARM (v. 2.01) was less suited for year-to-year grain yield prediction under dryland conditions but has potential as a tool for studying long-term interactions between environment and crop management system. Future development and applications of GPFARM must account for crop-specific responses to stress, detailed hydrology, better understanding of root uptake processes, and spatial variability to give more accurate grain yield predictions in water-stressed environments.

Technical Abstract: GPFARM is an ARS decision support system for strategic (long-term) planning. This study evaluated its performance for comparing alternative dryland no-till cropping systems and established limits of accuracy for eastern Colorado, using data collected in 1987 through 1999 from an on-going long-term experiment at three locations along a gradient of potential evapotranspiration (PET) [Sterling, low PET; Stratton, medium PET; and Walsh, high PET]. The crop rotations, which included winter wheat (Triticum aestivum L.), corn (Zea mays L.), sorghum [Sorghum bicolor (L.) Moench], proso millet (M) (Panicum miliaceum L.), and varying fallow periods, were: wheat-fallow, wheat-corn-fallow, and wheat-corn-millet-fallow at Sterling and Stratton; and wheat-fallow, wheat-sorghum-fallow, and wheat-sorghum-millet-fallow at Walsh. The ranges of relative error (RE) of simulated mean and root mean square error (RMSE) were: total soil profile water content (RE: 0 to 23%; RMSE: 38 to76 mm water); dry mass grain yield (RE: -27 to 84%; RMSE: 419 to 2567 kg ha-1); dry mass crop residue (RE: -5 to 42%; RMSE: 859 to 1845 kg ha-1); and total soil profile residual nitrate-N (RE: -42 to 32%; RMSE: 26 to 78 kg ha-1). GPFARM simulations agreed with observed trends and showed that productivity and water use efficiency increased with cropping intensification and that Stratton was the most productive and Walsh the least. GPFARM (v. 2.01) was less suited for year-to-year grain yield prediction under dryland conditions but has potential as a tool for studying long-term interactions between environment and crop management system. Future development and applications of GPFARM must account for crop-specific responses to stress, detailed hydrology, better understanding of root uptake processes, and spatial variability to give more accurate grain yield predictions in water-stressed environments.