A meta-analysis of long-term effects of conservation agriculture on maize grain yield under rain-fed conditions

Conservation agriculture involves reduced tillage, permanent soil cover and crop rotations to enhance soil fertility and to supply food from a dwindling land resource. Recently, conservation agriculture has been promoted in Southern Africa, mainly for maize-based farming systems. However, maize yields under rain-fed conditions are often variable. There is therefore a need to identify factors that influence crop yield under conservation agriculture and rain-fed conditions. Here, we studied maize grain yield data from experiments lasting 5 years and more under rain-fed conditions. We assessed the effect of long-term tillage and residue retention on maize grain yield under contrasting soil textures, nitrogen input and climate. Yield variability was measured by stability analysis. Our results show an increase in maize yield over time with conservation agriculture practices that include rotation and high input use in low rainfall areas. But we observed no difference in system stability under those conditions. We observed a strong relationship between maize grain yield and annual rainfall. Our meta-analysis gave the following findings: (1) 92% of the data show that mulch cover in high rainfall areas leads to lower yields due to waterlogging; (2) 85% of data show that soil texture is important in the temporal development of conservation agriculture effects, improved yields are likely on well-drained soils; (3) 73% of the data show that conservation agriculture practices require high inputs especially N for improved yield; (4) 63% of data show that increased yields are obtained with rotation but calculations often do not include the variations in rainfall within and between seasons; (5) 56% of the data show that reduced tillage with no mulch cover leads to lower yields in semi-arid areas; and (6) when adequate fertiliser is available, rainfall is the most important determinant of yield in southern Africa. It is clear from our results that conservation agriculture needs to be targeted and adapted to specific biophysical conditions for improved impact

Comparative Evaluation of Some Crop Yield Prediction Models using Tropical Cowpea Yield-Weather Data

A computer program was adopted from the work of Hill et al. (1982) to calibrate and test three of the existing yield prediction models using tropical cowpea yieldÐweather data. The models tested were Hanks Model (first and second versions). Stewart Model (first and second versions) and HallÐButcher Model. Three sets of cowpea yield-water use and weather data were collected. The first one was used for calibration and the other two for validation. The results obtained from the models were compared with field values using correlation coefficient and mean error of deviation. Stewart first model had r-values of 0.93 and 0.74 and mean error of deviation of 0.1 and 0.15. The second model had r-values of 0.92 and 0.86 and mean error of deviation of 0.05 and 0.11. Hanks first model had r-values of 0.94 and 0.89 and average mean error of deviation of 0.03 and 0.10, the second model had r values of 0.93 and .096 and average mean error of deviation of 0.05 and 0.03. The r-values for Hall-Butcher model were 0.76 and 0.96. The mean errors of deviation were 0.14 and 0.03. Generally, Hanks model predicted better than the other models but may still need to be further modified to be able to predict well for tropical conditions. Keywords: Prediction model, yield prediction, yield parameters, Hanks model, Stewart model, Hall Ð Butcher model, cowpea yield, tropical climatic conditionsDiscovery and Innovation Vol. 19 (2) 2007: pp. 89-9

Effect of animal manure incorporation on erosion indices of two Nigerian agriculture soils

No Abstract. Discovery and Innovation Vol. 17(3&4) 2005: 138-14