Predicting the probability that higher profits could be achieved by adopting PA

Algorithms were developed to predict spatial variation of yield and quality within winter wheat crops intended for bread-making. Bayesian networks were used to predict spatial probability maps of yield and quality based on data sources including yield maps, fertiliser applications, soil variables and Sentinel 2 satellite data. Results presented here for five UK fields show that there was a 65% likelihood of achieving a grain protein premium with variable rate nitrogen application compared to 50% with uniform N. Achieving this premium would increase revenues by £150/ha. A similar comparison for five German fields did not demonstrate a higher probability of profit

High resolution wheat yield mapping using Sentinel-2

Accurate crop yield estimates are important for governments, farmers, scientists and agribusiness. This paper provides a novel demonstration of the use of freely available Sentinel-2 data to estimate within-field wheat yield variability in a single year. The impact of data resolution and availability on yield estimation is explored using different combinations of input data. This was achieved by combining Sentinel-2 with environmental data (e.g. meteorological, topographical, soil moisture) for different periods throughout the growing season. Yield was estimated using Random Forest (RF) regression models. They were trained and validated using a dataset containing over 8000 points collected by combine harvester yield monitors from 39 wheat fields in the UK. The results demonstrate that it is possible to produce accurate maps of within-field yield variation at 10 m resolution using Sentinel-2 data (RMSE 0.66 t/ha). When combined with environmental data further improvements in accuracy can be obtained (RMSE 0.61 t/ha). We demonstrate that with knowledge of crop-type distribution it is possible to use these models, trained with data from a few fields, to estimate within-field yield variability on a landscape scale. Applying this method gives us a range of crop yield across the landscape of 4.09 to 12.22 t/ha, with a total crop production of approx. 289,000 t

Evaluating the sustainability of urban agriculture projects

Evaluating the sustainability of urban agriculture projects. 5. International Symposium for Farming Systems Design (AGRO2015

Identification and quantification of drivers of forest degradation in tropical dry forests: a case study in Western Mexico

The intensity of forest degradation is linked to landowners' decisions on management of their shifting cultivation systems. Understanding the processes involved in this land use type is therefore essential for the design of sustainable forest management practices. However, knowledge of the processes and patterns of forest transition that result from this practice is extremely limited. In this study, we used spatially-explicit binary logistic regression to study the proximate factors that relate to forest degradation by combining biophysical and socio-economic variables. Our study region is within the Ayuquila Basin, in Western Mexico, a typical fragmented tropical dry forest landscape dominated by shifting cultivation. Through a survey and semi-structured interviews with community leaders, we obtained data on the forest resources and on the uses that people make of them. Detailed forest cover maps for 2004 and 2010 were produced from high-resolution SPOT 5 data, and ancillary geographical data were used to extract spatial variables. The degree of social marginalization of each community and the ratio of forest area to population size were the main factors positively correlated with the probability of the occurrence of forest degradation. Livestock management and use of fence posts by the communities were also positively associated with forest degradation. Among biophysical factors, forest degradation is more likely to occur in flatter areas. We conclude that local drivers of forest degradation include both socioeconomic and physical variables and that both of these factors need to be addressed at the landscape level while developing measures for activities related to REDD+. (C) 2015 Elsevier Ltd. All rights reserved

Improving the contribution of livestock to crop-animal systems in rainfed areas in Southeast Asia: Proceedings of a workshop

The second workshop of the Crop-Animal Systems Research Network (CASREN) project embodies continuing activities on increasing the contribution of livestock to improving productivity of crop-animal systems in Southeast Asia. This workshop report involves four sessions. Session one focusses on ecoregional research on crop-animal systems and provides background information relevant to the project. This includes presentations on crop-animal research perspectives, policy options for the livestock sector, GIS application for site characterisation and definition of research domains; methodologies to address all year round feeding systems, ex-ante analyses for technical options, and methodologies for data analysis. Session two is devoted to presentations on the results of the household surveys, and based on these, session three discusses the proposed interventions by individual countries. The latter two sessions involves considerable discussion in ensuring the relevance and implementation of the proposed interventions

Assessing the ability of Sentinel-2 derived vegetation indices to explain inter-field yield variation in the context of index insurance - A case study of paddy rice inHaryana and Odisha, India

Smallholder agriculture in the Global South is characterised by high degree of risk, which disincentivises investment in productivity gains and limits rural development. Index Insurance aims to overcome the limitations of traditional insurance to insurance farmers against exposure to climatic extremes. Based on two study sites in India, Haryana and Odisha, this study contributes to the technical aspect of improving the indices, more specifically on how field level yield can be estimated through Sentinel-2 derived VI variables and which design options are more suitable to create these variables. The study shows that the best variables alone can explain 20% of the inter-field grain yield variation and that the best combination of variables can explain 53%. Furthermore, the main findings of the study suggest that it is beneficial to test different triggering measures and that including variables from phenologically tailored phases and isolating the rice varieties significantly improves the results. Additional research is needed before the approach is suitable for individualised index insurance but compared to alternative data sources the method will likely pose an effective and scalable way to identify yield gaps and to specifically target policy interventions

The United Nations World Water Development Report 2016: Water and Jobs

Three out of four of the jobs worldwide are water-dependent. In fact, water shortages and lack of access may limit economic growth in the years to come, according to the 2016 United Nations World Water Development Report, Water and Jobs, launched on 22 March 2016, World Water Day, in Geneva. From its collection, through various uses, to its ultimate return to the natural environment, water is a key factor in the development of job opportunities either directly related to its management (supply, infrastructure, wastewater treatment, etc.) or in economic sectors that are heavily water-dependent such as agriculture, fishing, power, industry and health. Furthermore, good access to drinking water and sanitation promotes an educated and healthy workforce, which constitutes an essential factor for sustained economic growth. In its analysis of the economic impact of access to water, the report cites numerous studies that show a positive correlation between investments in the water sector and economic growth. It also highlights the key role of water in the transition to a green economy