Determinants of farmers’ decision to adopt or not adopt inorganic fertilizer in the savannas of northern Nigeria

Soil nutrient deficiency has hampered increased agricultural production in the savannas of northern Nigeria. It has been observed that inorganic fertilizer (IF) has the potential to reverse the situation. However, low adoption among the farmers has characterized IF in the savannas of northern Nigeria. The application rates have also fallen far lower than the rate recommended by research and extension, resulting in low crop yields. This paper investigates the factors that influence farmers' decision to adopt or not to adopt IF and to evaluate the elasticity of adoption. This information will help to prioritize the factors that affect IF adoption decisions and suggest pathways for effective promotion of IF. About 49% of the survey farmers adopted IF and the application rate ranges from 5.6 to 64.4 kg ha^–1 (with a mean of 24.1 kg ha^–1). The probability of adoption increases with increased targeting of: farmers from the Guinea savanna agroecological zone, younger farmers, better educated farmers, food secure farmers and net sellers of food grains, farmers who have diversified into many crops, farmers who perceive increase in the fertilizer needs of their crops, and farmers who apply large quantities of organic manure. Among others, the estimates of elasticity of adoption indicate that a 1% increase in the number of farmers who perceive an increase in the fertilizer needs of their crops results in 3.23% increase in the probability of IF adoption. The paper concludes with policy implications for strategies aimed at promoting IF in the savannas of Nigeria and similar ecologies elsewhere.     

Estimating fractional cover of photosynthetic vegetation, non-photosynthetic vegetation and bare soil in the Australian tropical savanna region upscaling the EO-1 Hyperion and MODIS sensors

Quantitative estimation of fractional cover of photosynthetic vegetation (f_PV), non-photosynthetic vegetation (f_NPV) and bare soil (f_BS) is critical for natural resource management and for modeling carbon dynamics. Accurate estimation of fractional cover is especially important for monitoring and modeling savanna systems, subject to highly seasonal rainfall and drought, grazing by domestic and native animals, and frequent burning. This paper describes a method for resolving f_PV, f_NPV and f_BS across the ~ 2 million km² Australian tropical savanna zone with hyperspectral and multispectral imagery. A spectral library compiled from field campaigns in 2005 and 2006, together with three EO-1 Hyperion scenes acquired during the 2005 growing season were used to explore the spectral response space for f_PV, f_NPV and f_BS. A linear unmixing approach was developed using the Normalized Difference Vegetation Index (NDVI) and the Cellulose Absorption Index (CAI). Translation of this approach to MODerate resolution Imaging Spectroradiometer (MODIS) scale was assessed by comparing multiple linear regression models of NDVI and CAI with a range of indices based on the seven MODIS bands in the visible and shortwave infrared region (SWIR) using synthesized MODIS surface reflectance data on the same dates as the Hyperion acquisitions. The best resulting model, which used NDVI and the simple ratio of MODIS bands 7 and 6 (SWIR3/SWIR2), was used to generate a time series of fractional cover from 16 day MODIS nadir bidirectional reflectance distribution function-adjusted reflectance (NBAR) data from 2000-2006. The results obtained with MODIS NBAR were validated against grass curing measurement at ten sites with good agreement at six sites, but some underestimation of f_NPV proportions at four other sites due to substantial sub-pixel heterogeneity. The model was also compared with remote sensing measurements of fire scars and showed a good matching in the spatio-temporal patterns of grass senescence and posterior burning. The fractional cover profiles for major grassland cover types showed significant differences in relative proportions of f_PV, f_NPV and f_BS, as well as large intra-annual seasonal variation in response to monsoonal rainfall gradients and soil type. The methodology proposed here can be applied to other mixed tree-grass ecosystems across the world.