Browsing by Subject "Scaling-up."

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Nutrient management and spatial variability of soils across scales and settlement schemes in Zimbabwe
(2010) Cobo Borrero, Juan Guillermo; Cadisch, Georg
Decline in soil fertility in Africa is one of the most limiting biophysical factors to agricultural productivity, as nutrient mining and low yields are strongly related. However, the high heterogeneity in management together with different biophysical, socio-economical and political conditions across each African agro-ecosystem make blanket recommendations difficult. Thus, acknowledging heterogeneity, and moreover quantifying it at different spatial scales, are the first steps to make adequate recommendations for the different actors. The goal of this thesis was to develop new methodological approaches to better understand nutrient management and spatial variability of soils across different scales in African agro-ecosystems, having various small-holder settlement schemes in Zimbabwe as a case study. Firstly, the thesis includes a literature review on nutrient balances in Africa, which was carried out to illustrate main approaches, challenges, and progress made, with emphasis on issues of scale. The review revealed that nutrient balances are widely used across the continent. The collected dataset from 57 peer-reviewed studies indicated, however, that most of the balances were calculated at plot and farm scale, and generated in East Africa. Data confirmed the expected trend of negative balances for N and K (>75% of studies had mean values below zero), while for P only 56% of studies showed negative mean balances. Several cases with positive nutrient balances indicated that soil nutrient mining cannot be generalized across the African continent. Land use systems of wealthier farmers and plots located close to homesteads mostly presented higher N and P balances than systems of poorer farmers (p<0.001) and plots located relatively farther away (p<0.05). Partial nutrient balances were significantly higher (p<0.001) than full balances calculated for the same systems, but the latter carried more uncertainties. The change in magnitude of nutrient balances from plot to continental level did not show any noticeable trend, which challenges prevailing assumptions that a trend exists. However, methodological differences made a proper inter-scale comparison of results difficult. Actually, the review illustrated the high diversity of methods used to calculate nutrient balances and highlighted the main pitfalls, especially when nutrient flows and balances were scaled-up. In fact, gathered information showed that despite some few initiatives, appropriate scaling-up methods are still incipient. In the next chapter, the nutrient balance approach was applied in NE Zimbabwe. Three smallholder villages located in a typical communal area (colonial settlement from 1948), and in old (1987) and new (2002) resettlement areas (post- land reform settlements), on loamy sand, sandy loam and clay soils, respectively, were selected to explore differences in natural resource management and land productivity. Focus group discussions and surveys were carried out with farmers. Additionally, farmers in three wealth classes per village were chosen for a detailed assessment of their main production systems. Maize grain yields (Mg ha-1) in the communal (1.5-4.0) and new resettlement areas (1.9-4.3) were similar but significantly higher than in the old resettlement area (0.9-2.7), despite lower soil quality in the communal area. Nutrient input use was the main factor controlling maize productivity in the three areas (R2=59-83%), while inherent soil fertility accounted for up to 12%. Partial N balances (kg ha-1 yr-1) were significantly lower in the new resettlement (-9.1 to +14.3) and old resettlement (+7.4 to +9.6) than in the communal area (+2.1 to +59.6) due to lower nutrient applications. P balances were usually negative. Consistently, maize yields, nutrient applications and partial N balances were higher for the high wealth class than in poorer classes. It is argued that effective policies supporting an efficient fertilizer distribution and improved soil management practices, with clearer rights to land, are necessary to avoid future land degradation and to improve food security in Zimbabwe, particularly in the resettlement areas. In the last chapter, the same three villages in NE Zimbabwe were sampled to determine the feasibility of integrating mid-infrared spectroscopy (MIRS) and geostatistics, as a way of facilitating landscape analysis and monitoring. A nested non-aligned design with hierarchical grids of 750, 150 and 30 m resulted in 432 sampling points across all villages. At each point, a composite topsoil sample was taken and analyzed by MIRS. Conventional laboratory analyses on 25-38% of the samples were used for the prediction of concentration values on the remaining samples through the application of MIRS - partial least squares regression models. Models were successful (R2≥0.89) for sand, clay, pH, total C and N, exchangeable Ca, Mg and effective CEC; but not for silt, available P, and exchangeable K and Al (R20.82). Minimum sample sizes required to accurately estimate the mean of each soil property in each village were calculated. With regard to locations, fewer samples were needed in the new resettlement area than in the other two areas; regarding parameters, least samples were needed for estimating pH and sand. Spatial analyses of soil properties in each village were undertaken by constructing standardized isotropic semivariograms, which were usually well described by spherical models. Spatial autocorrelation of most variables was displayed over ranges of 250-695 m. The nugget-to-sill ratios showed that overall spatial dependence of soil properties was: new resettlement > old resettlement > communal area; which was attributed to both intrinsic (e.g. texture) and extrinsic (e.g. management) factors. As a new approach, geostatistical analysis was performed directly using MIRS data, after principal component analyses, where the first three components explained 70% of the overall variability. Semivariograms based on these components showed that spatial dependence per village was similar to overall dependence identified from individual soil properties in each area. The first component (explaining 49% of variation) related well with all soil properties of reference samples (absolute correlation values of 0.55-0.96). This demonstrated that MIRS data could be directly linked to geostatistics for a broad and quick evaluation of soil spatial variability. Integrating MIRS with geostatistical analyses is a cost-effective promising approach, i.e. for soil fertility and carbon sequestration assessments, mapping and monitoring at landscape level.