Quantified soil evolution under shifting agriculture in southern Cameroon

Open Access JournalIn the tropical rain forest zone of Southern Cameroon, shifting cultivation and perennial plantations of cocoa are the main farming systems practiced by small-scale farmers to ensure subsistence food crop production and a small income. This research used scientific modeling tools to produce quantitative information on the evolution of soils under this shifting agricultural system. An analysis of farming system led to the development of a conceptual model of the spatio-temporal dynamics of shifting agriculture, including transition matrices of rotational cycles that guided the sampling strategy for the study of soil evolution under the system. The study of soil variability showed that 30–35% of the total variance of some topsoil (0–20 cm) properties was due to the influence of land use practices. Five soil properties (pH, calcium, available phosphorus, bulk density and organic carbon) that are the most sensitive to these agricultural practices were empirically modeled and linear/quadratic fractional rational functions were successfully fitted to time series soil variables to derive quantitative measures on temporal changes in soil with land use. Data and methods produced are useful for soil quality assessment and spatio-temporal dynamic simulation in order to guide decision-making for sustainable land-use planning and soil resources management

About regression-kriging: from equations to case studies

This paper discusses the characteristics of regression-kriging (RK), its strengths and limitations, and illustrates these with a simple example and three case studies. RK is a spatial interpolation technique that combines a regression of the dependent variable on auxiliary variables (such as land surface parameters, remote sensing imagery and thematic maps) with simple kriging of the regression residuals. It is mathematically equivalent to the interpolation method variously called ¿Universal Kriging¿ (UK) and ¿Kriging with External Drift¿ (KED), where auxiliary predictors are used directly to solve the kriging weights. The advantage of RK is the ability to extend the method to a broader range of regression techniques and to allow separate interpretation of the two interpolated components. Data processing and interpretation of results are illustrated with three case studies covering the national territory of Croatia. The case studies use land surface parameters derived from combined Shuttle Radar Topography Mission and contour-based digital elevation models and multitemporal-enhanced vegetation indices derived from the MODIS imagery as auxiliary predictors. These are used to improve mapping of two continuous variables (soil organic matter content and mean annual land surface temperature) and one binary variable (presence of yew). In the case of mapping temperature, a physical model is used to estimate values of temperature at unvisited locations and RK is then used to calibrate the model with ground observations. The discussion addresses pragmatic issues: implementation of RK in existing software packages, comparison of RK with alternative interpolation techniques, and practical limitations to using RK. The most serious constraint to wider use of RK is that the analyst must carry out various steps in different software environments, both statistical and GIS

Using spatial information to improve collective understanding of shared environmental problems at watershed level

The decrease in stream water quality due to intensive agriculture is an environmental problem of concern in various parts of the world. This problem may not be appropriately addressed due to insufficient knowledge of its causes, in particular, the locations of the primary pollution sources and the relative magnitude of the problem under different management scenarios. In some situations, this information may be known but not adequately communicated to, or perceived by, the stakeholders who should decide on corrective action. A participatory approach, which includes the negotiation between suppliers and users of information and visualisation of scenarios, could be a powerful tool to overcome these inadequacies. This paper describes the provision of spatial information and the results of spatially explicit pollution modelling exercise to stakeholders in a participatory workshop, and evaluates the extent to which this information influenced decision-making. Workshops were organised with farmers and extensionists in the west region of Santa Catarina State, Brazil. Spatial information (synoptic satellite image, orthophoto mosaic, location of pig producers) and results from a spatially explicit dynamic pollution model (AgNPS) for previously prepared scenarios were presented. Questionnaires were administered at four different times during the workshops to test participants¿ reactions to, and opinions of, the information provided. Participants were able to understand and react to the spatial information despite their lack of previous exposure to such materials. Both visualisation and discussion caused major shifts in perception of the problem and suggestions for solutions. Participatory visualisation of scenarios enhanced perception and increased understanding of the water pollution problem caused by intensive pig farming and stimulated the collective search for solution

The Use of land evaluation information by land use planners and decision-makers; a case study in Santa Catarina, Brazil

Land evaluation is the prediction of land performance over time under specific uses, to guide strategic land use decisions. Modern land evaluation has a 30 year history, yet the results have often been disappointing. Land users and planners have been reported to ignore land evaluations, perhaps reflecting poor quality, low relevance, or poor communication. To test the success of a large land evaluation exercise undertaken as part of micro-catchment project in Santa Catarina State, southern Brazil, we queried agricultural extensionists, considered as the primary land evaluation clients. We used a questionnaire with both structured and open questions, to determine their experiences with, and attitudes to, the current land evaluation method. The soil resource inventory and associated land evaluation had some usefulness, but were not in general used for their intended purpose, namely farm planning. This was mainly because they did not contain crucial information necessary to such planning in the actual context of the farmer taking decisions. The primary deficiencies were identified as: (1) no estimate of environmental degradation risk; (2) no financial analysis; (3) no social analysis of decision-makers' attitudes and preferences; (4) no risk assessment for weather, yields, profits and market; and (5) insufficiently-specific alternative land uses. These deficiencies could have been avoided with a demand-driven approach, evaluating and reporting according to the true needs and opportunities of the decision-makers

Building a near infrared spectral library for soil organic carbon estimation in the Limpopo National Park, Mozambique

Soil organic carbon (SOC) is a key soil property and particularly important for ecosystem functioning and the sustainable management of agricultural systems. Conventional laboratory analyses for the determination of SOC are expensive and slow. Laboratory spectroscopy in combination with chemometrics is claimed to be a rapid, cost-effective and non-destructive method for measuring SOC. The present study was carried out in Limpopo National Park (LNP) in Mozambique, a data- and access-limited area, with no previous soil spectral library. The question was whether a useful calibration model could be built with a limited number of samples. Across the major landscape units of the LNP, 129 composite topsoil samples were collected and analyzed for SOC, pH and particle sizes of the fine earth fraction. Samples were also scanned in a near-infrared (NIR) spectrometer. Partial least square regression (PLSR) was used on 1037 bands in the wavelength range 1.25–2.5 μm to relate the spectra and SOC concentration. Several models were built and compared by cross-validation. The best model was on a filtered first derivative of the multiplicative scatter corrected (MSC) spectra. It explained 83% of SOC variation and had a root mean square error of prediction (RMSEP) of 0.32% SOC, about 2.5 times the laboratory RMSE from duplicate samples (0.13% SOC). This uncertainty is a substantial proportion of the typical SOC concentrations in LNP landscapes (0.45–2.00%). The model was slightly improved (RMSEP 0.28% SOC) by adding clay percentage as a co-variable. All models had poorer performance at SOC concentrations above 2.0%, indicating a saturation effect. Despite the limitations of sample size and no pre-existing library, a locally-useful, although somewhat imprecise, calibration model could be built. This model is suitable for estimating SOC in further mapping exercises in the LNP