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plants ha Ϫ1 and maximum grain yield at 74 100 plants ha Ϫ1 . However, maximum forage yields have also been Corn (Zea mays L.) hybrid selection and plant density are imporreported at 79 000 plants ha Ϫ1 (Graybill et al., 1991) and tant management considerations for successful forage production in dairy and livestock operations. The objectives of this study were (i) 100 000 plants ha Ϫ1 (Sparks, 1988).to determine the effect of plant density on high-and low-quality corn Even though corn forage yield may have a greater hybrids and (ii) to describe the economic trade-off between plant optimum plant density than corn for grain, forage qualdensity and forage yield and quality. Two adapted hybrids selected ity losses at high plant density have been reported for high and low quality characteristics were grown in the field at (McAllan and Phipps, 1977). As plant density increases five plant densities ranging from 44 500 to 104 500 plants ha Ϫ1 at six from 18 500 to 143 300 plants ha Ϫ1 , in vitro true digestlocations in Wisconsin during 1994, 1995, and 1996. Forage quality ibility decreases (Sanderson et al., 1995; Jones et al., response among hybrids was similar across the range of plant densities 1995). The negative relationship between plant density evaluated. As plant density increased, dry matter yield increased 1.7 and corn forage quality makes it difficult to recommend to 4.1 Mg ha Ϫ1 , depending on location. Maximum dry matter yields plant density for optimum animal performance based were observed at 97 300 to 102 200 harvested plants ha Ϫ1 . In vitro true digestibility decreased 16 to 23 g kg Ϫ1 as plant density increased. on yield. The objectives of this study were (i) to deter-Crude protein decreased 6 to 8 g kg Ϫ1 as plant density increased. mine the effect of plant density on high-and low-quality Neutral-detergent fiber increased 20 to 35 g kg Ϫ1 , and acid-detergent corn hybrids and (ii) to describe the economic tradefiber increased 19 to 29 g kg Ϫ1 with increasing plant density. A tradeoff between plant density and forage yield and quality. off exists between yield and quality for corn forage. Milk Mg Ϫ1 decreased 98 to 143 kg milk Mg Ϫ1 with increasing plant densities, but MATERIALS AND METHODS milk ha Ϫ1 increased 926 to 2176 kg milk ha Ϫ1 until about 75 000 to 85 000 harvested plants ha Ϫ1 , and did not change with higher plant den-Experiments were conducted from 1994 to 1996 at six locasities.tions in Wisconsin. The locations were grouped into three production zones. In the southern zone, the soil at Lancaster was a Rozetta silt loam (fine-silty, mixed, mesic Typic Hapludalf), and the soil at Arlington was a Plano silt loam (finewere fed genotypes with low and high digestibility. Like-Milk, kg ha Ϫ1 y ϭ 6720 ϩ 166x Ϫ 1.03x 2 0.96 wise, these hybrids yielded 2286, 1947, and 248 more kg † DM, dry matter; HI, harvest index; IVTD, in vitro true digestibility; CP, milk ha Ϫ1 in the southern, central, and northern zones, crude protein; NDF, neutral-detergent fiber; ADF, acid-detergent fiber; CW digestibi...

Th e modeling of yield response to water is expected to play an increasingly important role in the optimization of crop water productivity (WP) in agriculture. During 3 yr (2004)(2005)(2006)(2007), fi eld experiments were conducted to assess the crop response to water stress of quinoa (Chenopodium quinoa Willd.) in the Bolivian Altiplano (4000 masl) under diff erent watering conditions (from rain fed, RF, to full irrigation, FI). Crop physiological measurements and comparisons between simulated and observed soil water content (SWC), canopy cover (CC), biomass production, and fi nal seed yield of a selected number of fi elds were used to calibrate the AquaCrop model. Subsequently, the model was validated for diff erent locations and varieties using data from other experimental fi elds and from farmers' fi elds. Additionally, a sensitivity analysis was performed for key input variables of the parameterized model. AquaCrop simulated well the decrease of the harvest index (HI) of quinoa in response to drought during early grain fi lling as observed in the fi eld. Further-on, the procedure for triggering early canopy senescence was deactivated in the model as observed in the fi eld. Biomass WP (g m −2 ) decreased by 9% under fully irrigated conditions compared with RF and defi cit irrigation (DI) conditions, most probably due to severe nutrient depletion. Satisfactory results were obtained for the simulation of total biomass and seed yield [validation regression R 2 = 0.87 and 0.83, and Nash-Sutcliff effi ciency (EF) = 0.82 and 0.79, respectively]. Sensitivity analysis demonstrated the robustness of the AquaCrop model for simulation of quinoa growth and production, although further improvements of the model for soil nutrient depletion, pests, diseases, and frost are also possible. available water in the soil between fi eld capacity and permanent wilting point; WP, water productivity; WP*, water productivity, normalized for ET o ; Y, total seed yield; Z x , maximum rooting depth.