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CROP ECOLOGY, MANAGEMENT & QUALITY

Evaluation of the Effect of Density on Potato Yield and Tuber Size Distribution

Dep. of Horticulture and Dep. of Agriculture and Applied Economics, Univ. of Wisconsin-Madison, 1575 Linden Dr., Madison, WI 53706

^* Corresponding author (ajbussan{at}wisc.edu).

Potato (Solanum tuberosum) yield has been optimized for in-row spacings ranging from 15 to 40 cm depending on region, targeted market, variety, and other factors. Production goals require optimizing tuber size to maximize crop value. Our goal was to evaluate the effect of plant, stem, and tuber density on stem and tuber set, potato yield, tuber size distribution, and other quality factors. Research plots were established within a 20-ha commercial production field, and analysis was done with linear and nonlinear regression. Plant density decreased with increasing in-row plant spacing. Stem density increased linearly with increasing plant density, but response differed across years. Tuber density increased to a maximum of 190 tubers m^–2 in response to plant and stem density, with stem density more accurately predicting tuber set. Yield was related to plant, stem, and tuber density using nonlinear regression, more accurately predicted by stem and tuber density than by plant density. A hyperbolic model was used to predict yield with estimated maximum yield of 86 Mg ha^–1 when related to stem density. Average tuber size was related to stem and tuber density using the inverse yield law and estimated maximum average tuber size of >200 g. The distribution for tuber sizes was estimated as a Weibull probability density function that predicted changes in tuber size in response to stem and tuber density. The hyperbolic model accurately predicted tuber density and yield with the added benefit that estimated parameters have biological importance, unlike polynomial or other regression models used to predict crop yield. Modeling tuber size distribution over different stem densities provides a mechanism for future economic analysis to optimize management and conduct sensitivity analysis to determine the most important factors influencing crop value.