Different methods, different wilds: Evaluating alternative mappings of wildness using fuzzy MCE and Dempster-Shafer MCE

Different multi-criteria evaluation (MCE) approaches are applied to a fuzzy wildness mapping problem in Scotland. The result of fuzzy weighted linear combination and fuzzy order weighted averaging approaches are compared with the application of a Dempster-Shafer MCE. We discuss the implications of different approaches in light of decision making associated with suitability in a context where (i) suitability (wildness) may not be very well defined, (ii) the decision makers may not fully understand the informatics aspects associated with applying weights, but (iii) require decisions to be accountable and transparent. In such situations we suggest that the outputs of Dempster-Shafer MCE may be more appropriate than a fully fuzzy model of suitability

Using control data to determine the reliability of volunteered geographic information about land cover

There is much interest in using volunteered geographic information (VGI) in formal scientific analyses. This analysis uses VGI describing land cover that was captured using a web-based interface, linked to Google Earth. A number of control points, for which the land cover had been determined by experts allowed measures of the reliability of each volunteer in relation to each land cover class to be calculated. Geographically weighted kernels were used to estimate surfaces of volunteered land cover information accuracy and then to develop spatially distributed correspondences between the volunteer land cover class and land cover from 3 contemporary global datasets (GLC-2000, GlobCover and MODIS v.5). Specifically, a geographically weighted approach calculated local confusion matrices (correspondences) at each location in a central African study area and generated spatial distributions of user's, producer's, portmanteau, and partial portmanteau accuracies. These were used to evaluate the global datasets and to infer which of them was ‘best’ at describing Tree cover at each location in the study area. The resulting maps show where specific global datasets are recommended for analyses requiring Tree cover information. The methods presented in this research suggest that some of the concerns about the quality of VGI can be addressed through careful data collection, the use of control points to evaluate volunteer performance and spatially explicit analyses. A research agenda for the use and analysis of VGI about land cover is outlined

Cost of the proposed scenarios expressed as the percentage of Agricultural GDP.

<p>Cost of the proposed scenarios expressed as the percentage of Agricultural GDP.</p

Affordable Nutrient Solutions for Improved Food Security as Evidenced by Crop Trials

<div><p>The continuing depletion of nutrients from agricultural soils in Sub-Saharan African is accompanied by a lack of substantial progress in crop yield improvement. In this paper we investigate yield gaps for corn under two scenarios: a micro-dosing scenario with marginal increases in nitrogen (N) and phosphorus (P) of 10 kg ha⁻¹ and a larger yet still conservative scenario with proposed N and P applications of 80 and 20 kg ha⁻¹ respectively. The yield gaps are calculated from a database of historical FAO crop fertilizer trials at 1358 locations for Sub-Saharan Africa and South America. Our approach allows connecting experimental field scale data with continental policy recommendations. Two critical findings emerged from the analysis. The first is the degree to which P limits increases in corn yields. For example, under a micro-dosing scenario, in Africa, the addition of small amounts of N alone resulted in mean yield increases of 8% while the addition of only P increased mean yields by 26%, with implications for designing better balanced fertilizer distribution schemes. The second finding was the relatively large amount of yield increase possible for a small, yet affordable amount of fertilizer application. Using African and South American fertilizer prices we show that the level of investment needed to achieve these results is considerably less than 1% of Agricultural GDP for both a micro-dosing scenario and for the scenario involving higher yet still conservative fertilizer application rates. In the latter scenario realistic mean yield increases ranged between 28 to 85% in South America and 71 to 190% in Africa (mean plus one standard deviation). External investment in this low technology solution has the potential to kick start development and could complement other interventions such as better crop varieties and improved economic instruments to support farmers.</p> </div

Cereal yield trends since 1960 in Africa, Sub-Sahara Africa (SSA), South America and Asia.

<p>Cereal yield trends since 1960 in Africa, Sub-Sahara Africa (SSA), South America and Asia.</p

Relationship between historic FAO experimental corn field trials with at least five N and P input combinations and corn yields calculated with the Mitscherlich-Baule crop response function totaling 1358 unique nutrient-yield relations (r² = 0.94).

<p>Relationship between historic FAO experimental corn field trials with at least five N and P input combinations and corn yields calculated with the Mitscherlich-Baule crop response function totaling 1358 unique nutrient-yield relations (r² = 0.94).</p

Crosses indicate locations of 1358 historic FAO corn field trials with at least five N and P input combinations in Africa and South America carried out between 1969 and 1993.

<p>Colors indicate (subnational) maize (corn) yields (ton/ha) as collected by <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0060075#pone.0060075-IFPRI1" target="_blank">[23]</a>.</p

The median, 25^th and 75^th percentile values from the distributions of the Mitscherlich-Baule crop response function parameters (a₁, a₂, a₃ and a₄) fitted for the 1358 individual crop trials.

<p>The median, 25^th and 75^th percentile values from the distributions of the Mitscherlich-Baule crop response function parameters (a₁, a₂, a₃ and a₄) fitted for the 1358 individual crop trials.</p

A typical example of a fitted crop response trial with experimental (blue line with circles) and modeled data (black line with squares) with eight N and P input combinations and resulting yields.

<p>A typical example of a fitted crop response trial with experimental (blue line with circles) and modeled data (black line with squares) with eight N and P input combinations and resulting yields.</p

Mean corn yield increase (%) across trial sites at additional applications of 10 kg N ha⁻¹, 10 kg P ha⁻¹ or 10 kg N and P ha⁻¹ (error bars refer to the standard deviation of the obtained yield increases observed across all trials).

<p>Mean corn yield increase (%) across trial sites at additional applications of 10 kg N ha⁻¹, 10 kg P ha⁻¹ or 10 kg N and P ha⁻¹ (error bars refer to the standard deviation of the obtained yield increases observed across all trials).</p