Pedometric mapping of soil organic matter using a soil map with quantified uncertainty

This paper compares three models that use soil type information from point observations and a soil map to map the topsoil organic matter content for the province of Drenthe in the Netherlands. The models differ in how the information on soil type is obtained: model 1 uses soil type as depicted on the soil map for calibration and prediction; model 2 uses soil type as observed in the field for calibration and soil type as depicted on the map for prediction; and model 3 uses observed soil type for calibration and a pedometric soil map with quantified uncertainty for prediction. Calibration of the trend on observed soil type resulted in a much stronger predictive relationship between soil organic matter content and soil type than calibration on mapped soil type. Validation with an independent probability sample showed that model 3 out‐performed models 1 and 2 in terms of the mean squared error. However, model 3 over‐estimated the prediction error variance and so was too pessimistic about prediction accuracy. Model 2 performed the worst: it had the largest mean squared error and the prediction error variance was strongly under‐estimated. Thus validation confirmed that calibration on observed soil type is only valid when the uncertainty about soil type at prediction sites is explicitly accounted for by the model. We conclude that whenever information about the uncertainty of the soil map is available and both soil property and soil type are observed at sampling sites, model 3 can be an improvement over the conventional model 1.     

Evaluation of agricultural ecosystem services in fallowing land based on farmers’ participation and model simulation

Fallowing with green fertilizer can benefit agricultural ecosystem services (AES). Farmers in Taiwan do not implement fallow practices and plant green fertilizer because the current subsidy level (46,000 NT$ per ha) is too low to manage fallowing. This paper defines the objective of government agriculture policy or the farmer’s objective as maximization of farm productivity, approximated to the value of social welfare and AES. Farms, which do not follow proper fallowing practices, often have poorly maintained fallow land or left farmland abandoned. This results in negative environmental consequences such as cutworm infestations in abandoned land, which in turn can affect crops in adjacent farmlands. The objectives of this study are twofold. First, it determines the proper fallowing subsidy based on the concept of payment for ecosystem services to entice more farmers to participate in fallowing. Second, it simulates the benefit of planting green manure in fallow land to the supply of AES based on the rate of farmers who are willing to participate in fallow land practices and essential parameters that can affect soil fertility change. The approach involves a series of interviews and a developed empirical model. The value of AES when the rate of farmer participation is 100% represents a 1.5% increase in AES (448,317,000 NT$) over the value at the current participation rate of 14%. This study further concludes that the appropriate fallowing subsidy has a large positive impact on AES and social welfare (e.g., benefit from food and biofuel supplies) and is seen as a basis of ecological governance for sustainable agro-ecosystems.     

Exante assessment of dual-purpose sweet potato in the crop–livestock system of western Kenya: A minimum-data approach

Mixed crop–livestock systems have a crucial role to play in meeting the agricultural production challenges of smallholder farmers in sub-Saharan Africa. Sweet potato is seen as a potential remedial crop for these farmers because of its high productivity and low input requirements, while its usefulness for both food and feed (dual-purpose) make it attractive in areas where land availability is declining. In this paper, we develop and apply a ‘minimum-data’ methodology to assess exante the economic viability of adopting dual-purpose sweet potato in Vihiga district, western Kenya. The methodology uses and integrates available socio-economic and bio-physical data on farmers’ land use allocation, production, and input and output use. Spatially heterogeneous characteristics of the current system regarding resources and productivity are analyzed to assess the profitability of substituting dual-purpose sweet potato for other crops currently grown for food and feed. Results indicate that a substantial number of farmers in the study area could benefit economically from adopting dual-purpose sweet potato. Depending on assumptions made, the adoption rate, expressed as the percentage of the total land under adopting farms, is between 55% and 80%. The analysis shows that the adoption rate is likely to vary positively with the average total yield of dual-purpose sweet potato, the harvest index (the ratio between tuber and fodder yields), the price of milk, and the nutritional value of available fodder. This study demonstrates the usefulness of the minimum-data methodology and provides evidence to support the hypothesis that dissemination of the dual-purpose sweet potato could help improve the livelihoods of smallholder farmers operating in mixed crop–livestock systems in east Africa.     

Comparison of disease patterns assessed by three independent surveys of cassava mosaic virus disease in Rwanda and Burundi

Cassava mosaic disease (CMD) seriously affects cassava yields in Africa. This study compared the spatial distribution of CMD using three independent surveys in Rwanda and Burundi. Geostatistical techniques were used to interpolate the point‐based surveys and predict the spatial distributions of different measures of the disease. Correlative relationships were examined for 35 environmental and socio‐economic spatial variables of which 31 were correlated to CMD intensity, with the highest correlation coefficients for latitude (?0·47), altitude (?0·36) and temperature (+0·36). The most significant explanatory variables were entered in separate linear regression models for each of the surveys. The models explained 54%, 44% and 22% of the variation in CMD. The residuals of the regression models were interpolated using kriging and added to the regression models to map CMD across both countries. Significant differences were calculated in some areas after correcting for interpolation error. An important explanation of the differences is interaction between the CMD pandemic and the dates of the three surveys. Large relative prediction errors obtained in the regression kriging procedure show the need to improve the survey design and decrease measurement error. Improved maps of crop diseases such as CMD could aid targeting of control interventions and thereby contribute to increasing crop yields. This study validated the unique character of each of the survey approaches adopted and underlines the importance of specific interpretation of results for CMD management. The study emphasizes the need for optimization of sampling designs and survey protocols to maximize the potential of regression kriging.     

Tools for optimizing management of spatially-variable fields

Efficient use of agro-chemicals is beneficial for farmers as well as for the environment. Spatial and temporal optimization of farm management will increase productivity or reduce the amount of agro-chemicals. This type of management is referred to as Precision Agriculture. Traditional management implicitly considers any field to be a homogeneous unit for management: fertilization, tillage and crop protection measures, for example, are not varied within a single field. The question for management is what to do when. Because of the variability within the field, this implies inefficient use of resources. Precision agriculture defines different management practices to be applied within single, variable fields, potentially reducing costs and limiting adverse environmental side effects. The question is not only what and when but also where. Many tools for management and analysis of spatial variable fields have been developed. In this paper, tools for managing spatial variability are demonstrated in combination with tools to optimize management in environmental and economic terms. The tools are illustrated on five case studies ranging from (1) a low technology approach using participatory mapping to derive fertilizer recommendations for resource-poor farmers in Embu, Kenya, (2) an example of backward modelling to analyze fertilizer applications and restrict nitrogen losses to the groundwater in the Wieringermeer in The Netherlands, (3) a low-tech approach of precision agriculture, developed for a banana plantation in Costa Rica to achieve higher input use efficiency and insight in spatial and temporal variation, (4) a high-tech, forward modelling approach to derive fertilizer recommendations for management units in Zuidland in The Netherlands, and (5) a high-tech, backward modelling approach to detect the relative effects of several stress factors on soybean yield.     

Optimizing land use distribution to minimize nutrient depletion: a case study for the Atlantic Zone of Costa Rica

Nutrient depletion may be reduced by effective land use planning using a nutrient balance model and linear programming techniques to optimize the distribution of land use over different land units. This technique plans the geographical distribution of land utilization types and creates a more sustainable basis for agriculture in an area. In contrast with traditional land use planning where land utilization types are matched with land units on the basis of maximizing present agricultural production, this approach focuses on long-term effects.

A case study was carried out in an area of 3340 ha in the southern part of a settlement scheme in the Atlantic Zone of Costa Rica. Land units vary between young fertile volcanic soils and relatively old, leached unfertile soils. Land utilization types vary between extensively grazed pasture and intensively cultivated annual and perennial crops. Information on land units with its land qualities and on land utilization types was stored in a geographical information system which was integrated with a nutrient balance model (NUTBAL). This model, initially developed for Kenya and calibrated for local conditions in Costa Rica, was based on separate assessments of 5 nutrient input factors: mineral fertilizers, organic manure, wet and dry deposition, nitrogen fixation and sedimentation, and 5 nutrient output factors: harvested product, crop residue removal, leaching, denitrification and erosion. For every combination of land unit and land utilization type the nutrient balance was modelled. With a linear programming model, the land utilization types were distributed over the different land units, such that nutrient depletion was minimized in the area within a set of boundary conditions. Two different scenarios were elaborated. One in which all land utilization types were redistributed and one in which the location of forest areas was fixed at their present location.

The optimized land use distribution based on current land use types was compared with the actual distribution which had an average nutrient depletion of 22, 5 and 13 kg ha⁻¹ yr⁻¹ for respectively N, P and K. There was little difference in nutrient depletion between the two scenarios. The first scenario resulted in a nutrient depletion of 18, 3 and 9 kg ha⁻¹ yr⁻¹ and the second one in 19, 3 and 10 kg ha⁻¹ yr⁻¹ for N, P and K respectively. However, there was a large difference with the actual situation on one hand and the two scenarios on the other. In both cases there was a slight increase in the estimated agricultural production. The introduction of a nutrient depletion model in land use planning seems to constitute a useful complement to the existing procedures.     

Economic potential for soil carbon sequestration in the Nioro region of Senegal’s Peanut Basin

This paper presents results from an analysis of the economic potential for soil carbon sequestration in the Nioro region of Senegal’s Peanut Basin. This analysis was based on the linkage of site-specific biophysical models and economic simulation models using the Tradeoff Analysis System to simulate farmers’ participation in contracts to sequester soil carbon. Available soils and climate data were used to implement the DSSAT/Century models to estimate crop yields and changes in soil carbon stocks under nine scenarios of increased fertilizer use and increased incorporation of crop residues in a peanut-millet rotation. Data from the 2001 farm survey conducted by the Ecole Nationale d’Economie Appliquée were used to parameterize a spatially explicit econometric-process simulation model for the peanut-millet production system. The economic simulation model was used to simulate a carbon-payment scheme that requires farmers to apply higher fertilizer rates and incorporate some crop residues into the soil. The results show that the combination of increased fertilizer use and crop residue incorporation could result in the supply of marketable quantities of carbon that could be sequestered in the soils of the Nioro region. However, the sensitivity of results to the levels of labor costs of incorporating crop residues, the value of crop residues, and the transaction costs of implementing carbon payment schemes, suggests the need for better data on these variables and for an accurate assessment of the capabilities of local institutions to implement carbon contracts.     

Uncertainty analysis as a tool for refining land dynamics modelling on changing landscapes: a case study in a Spanish Natural Park

In this study we developed a methodology aimed at improving the assessment of inter-annual land cover dynamics from hard classified remotely sensed data in heterogeneous and resilient landscapes. The methodology is implemented for the Spanish Natural Park of Sierra de Ancares, where human interference during the last century has resulted in the destruction and fragmentation of the original land cover. We ran supervised classifications, with a maximum likelihood algorithm (Maxlike), on a temporal series of Landsat images (1991–2005), followed by an uncertainty assessment using fuzzy classifications and confusion indices (CIs). This allowed us to show how much (and where) of the resulting maps contained a substantial amount of error, distinguishing data that might be useful to measure land change from data that are not particularly useful when applying a post-classification comparison methodology. In this way, we can detect true changes not skewed by the effects of uncertainty. Even if patterns of change were always coherent amongst years, they were more realistic after reducing uncertainty, in spite of a substantial decrease in the number of available pixels (i.e. unmasked by the method). We then computed land cover dynamics by means of a model specifically designed to determine the frequency of disturbances (mainly fire events) and the vegetation recovery time during the study period. Model outputs showed correlated landscape patterns at a broad scale and provided useful results to explore land cover change from pattern to process.