Production curves for pastures and veld

The Gompertz curve applied to forage cumulative yields can be specified with three or more data points (yields at known times, including a possible zero yield at the start). Good correlations between pasture experimental data and the Gompertz curve, fitted by a weighting iterative procedure, have been obtained.     

Response of tilapia yield and economics to varying rates of organic fertilization and season in two Central American countries

The response of Nile tilapia (Oreochromis niloticus) yield to weekly applications of chicken litter at 125, 250, 500 or 1000 kg total solids (T.S.)/ha was determined in Honduras and Panama using a completely randomized design. Tilapia were stocked at 10 000/ha into 0.1-ha (Honduras) and 0.087-ha (Panama) earthen ponds. Each experiment, which lasted approximately 150 days, was performed during the rainy and dry season. Enterprise budgets were developed for each fertilization rate in each country.Gross yield of tilapia (y) increased significantly with chicken litter applications (x) in both countries, and was described by the model y=797.3+2.945x−0.001x² (r²=0.775; n=48). Gross yields ranged from 827–2729 kg/ha in 147 days during the rainy season, and from 1145–2984 kg/ha in 150 days during the dry season. Maximum tilapia gross yields were achieved at 1000 kg T.S./ha week⁻¹ chicken litter in both countries. In Honduras, rainy (1761 kg/ha in 152 days) and dry (1705 kg/ha in 150 days) season mean tilapia gross yields were similar (P=0.05). Dry season (2071 kg/ha in 149 days) mean tilapia gross yield in Panama was significantly greater (P<0.05) than rainy season mean gross yield (1683 kg/ha in 141 days). Rainy season climatic conditions in Panama probably contributed to the lower fish yields. Mean fish gross yield at the cooler, drier Honduras site (1733 kg/ha in 151 days), an upland valley located 580 m above sea level, and at the Panama site (1855 kg/ha in 145 days), a coastal plateau 100 m above sea level, was similar (P=0.05). Mean gross yields were similar in both countries for all but the highest fertilization rate, where the Panama mean yield was significantly greater. This difference was caused by site-specific factors other than nutrient input.The use of chicken litter as an organic fertilizer was profitable in both Honduras and Panama. Net returns to land, labor and management during the 5.5-month production cycle ranged from $ 642 to $ 1724/ha (Honduras) or from −$237 to $313/ha (Panama) for the low to high fertilization rates, respectively. Application of 1000 kg T.S./ha week⁻¹ chicken litter yielded the greatest estimated profit in both countries.     

Reducing Catfish Farm Losses Due to Dockages Assessed by Processing Plants

Dockages can have a significant effect on catfish, Ictalurus punctatus, farm revenues. This study was conducted to quantify common dockages, examine seasonal and yearly variations in dockages assessed, and determine optimal production practices given various dockage scenarios. A convenience survey of invoice records from 30 commercial catfish farms and 10 processing plants provided 3686 daily catfish load records that were used to quantify dockages. A linear programming model was developed to examine optimal production practices given 11 alternative production scenarios with five size‐grading technologies subject to 24 types and levels of dockages. The survey revealed that 95% of catfish loads delivered to processing plants between 1997 and 2002 were assessed dockages that resulted in average losses of 2.45% per load or $0.066/kg of catfish marketed over the study period. Out‐of‐size discounts constituted the greatest losses. Dockage losses can be reduced by shifting either to longer‐term single‐batch production or more intensive grading. Longer‐term production results in fewer smaller fish that would incur dockage losses. However, cash flow constraints require more intensive early‐season grading. The grader choice depended on the dockage tolerance level and rate, the frequency distribution of sizes of catfish in the population, the efficiency of the grading technology, and the cost of the grading method. Larger farms minimize losses with intensive active grading (University of Arkansas at Pine Bluff grader).