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Agronomic Modeling

Using the CROPGRO-Peanut Model to Quantify Yield Gaps of Peanut in the Guinean Savanna Zone of Ghana

^a Savanna Agric. Res. Inst., Tamale, Ghana
^b Int. Crops Res. Inst. for the Semi-Arid Tropics (ICRISAT), Patancheru, Andra Pradesh 502 324, India
^c Dep. of Agron., Univ. of Florida, Gainesville, FL 32611-0500
^d Dep. of Agric. and Biol. Eng., Univ. of Florida, Gainesville, FL 32611-0500
^e Deceased, Savanna Agric. Res. Inst., Tamale, Ghana

^* Corresponding author (kjb{at}mail.ifas.ufl.edu)

Received for publication January 28, 2003. Peanut (Arachis hypogaea L.) yield in Ghana is limited by variable rainfall, low soil fertility, pests and diseases, and poor crop management. Field experiments were conducted during the 1997 and 1998 seasons at the Savanna Agricultural Research Station in Ghana to evaluate the CROPGRO-peanut model for its ability to simulate growth, yield, and soil water balance of a peanut crop and to quantify yield losses caused by biotic and abiotic factors. Two peanut cultivars, Chinese which matures in 90 d, and F-Mix which matures in 120 d, were grown rainfed on an Alfisol soil at three sowing dates between May and August in 1997 and at four dates in 1998. Soil water and crop growth were measured during the season and compared with crop model simulations to determine yield-limiting factors relative to potential yield. Growth and yield were highest for the early sowing dates and decreased progressively with later sowing, a trend attributed to leaf diseases. After incorporating functions for percentage leaf defoliation and percentage diseased leaf area, the model accurately simulated soil water content fluctuations, crop growth, and yield of cultivars for the sowing dates and seasons. Simulated yield losses caused by water deficits were small (averaging 5–10%) for early sowing dates (late May to mid-July) and increased with later sowing dates (20 and 70% for third and fourth sowings). Yield losses due to diseases and pests were simulated as a percentage of potential yield under water-limited environments and averaged 40%, also increasing with later sowing dates. Using 13 yr of weather data, simulated yields were reduced 10 to 20% by water deficit for the two earlier (normal) sowing dates, but more for the later sowing dates, while additional yield reductions were attributed to biotic stresses. We conclude that the CROPGRO-peanut model can be successfully used to quantify the yield potential and yield gaps associated with yield-reducing stresses and crop management for this region.

Abbreviations: CN2, runoff curve number • DUL, drained upper limit • LL, lower limit of plant available water • PESW, plant extractable soil water • RMSE, root mean square error • SAT, saturated upper limit • SWCON, fraction of water above field capacity drained per day • WR, root weighting function