Estimating smallholder crops production at village level from Sentinel-2 time series in Mali's cotton belt

In Mali's cotton belt, spatial variability in management practices, soil fertility and rainfall strongly impact crop productivity and the livelihoods of smallholder farmers. To identify crop growth conditions and hence improve food security, accurate assessment of local crop production is key. However, production estimates in heterogeneous smallholder farming systems often rely on labor-intensive surveys that are not easily scalable, nor exhaustive. Recent advances in high-resolution earth observation (EO) open up new possibilities to work in heterogeneous smallholder systems. This paper develops a method to estimate individual crop production at farm-to-community scales using high-resolution Sentinel-2 time series and ground data in the commune of Koningue, Mali. Our estimation of agricultural production relies on (i) a supervised, pixel-based crop type classification inside an existing cropland mask, (ii) a comparison of yield estimators based on spectral indices and derived leaf area index (LAI), and (iii) a Monte Carlo approach combining the resulting unbiased crop area estimate and the uncertainty on the associated yield estimate. Results show that crop types can be mapped from Sentinel-2 data with 80% overall accuracy (OA), with best performances observed for cotton (Fscore 94%), maize (88%) and millet (83%), while peanut (71%) and sorghum (46%) achieve less. Incorporation of parcel limits extracted from very high-resolution imagery is shown to increase OA to 85%. Obtained through inverse radiative transfer modeling, Sen2-Agri estimates of LAI achieve better prediction of final grain yield than various vegetation indices, reaching R2 of 0.68, 0.62, 0.8 and 0.48 for cotton, maize, millet and sorghum respectively. The uncertainty of Monte Carlo production estimates does not exceed 0.3% of the total production for each crop type

Distribution and amounts of nitrous and nitric oxide emissions from British soils

This study establishes an empirical predictive model of N20 and NO emissions for Great Britain based on multivariate regression analysis of field measurement data from several studies in European countries and the USA for which the results have been published in the last 18 years. The significance of studying the emissions of these gases is due to the role of N20 as a greenhouse effect gas and NO participation in reactions with ozone. Soils are known to be an important source of N2O and also contribute significant amounts of NO into the atmosphere. Knowledge of N20 and NO emissions from soils at a national scale is important due to the signed international agreements which oblige Great Britain to produce inventories of greenhouse effect gases and monitor the emissions of NOx gases. The field studies observed the relationships between the emissions and their controlling factors and on the basis of those relationships, national modelling approaches to predicting the amounts of emissions have been defined. Due to the highly variable nature of emissions, more than one empirical model was developed for each of the gases. The relationships defined in the analysis were later applied to estimate N20 and NO emissions from British soils with an application of input parameter data of the established controlling factors in the framework of Arclnfo GRID.Data for N fertiliser input, soil moisture and temperature were not readily available and therefore had to be estimated with existing data. Soil moisture was predicted with the SPACTeach model based on the monthly precipitation sums obtained from the Climate LINK data set. This data source also provided monthly air temperature data used to model soil temperature with the theory of heat flux. N input was estimated as a sum of mineral and organic N fertiliser inputs from agriculture and atmospheric N deposition. The former was estimated from the recommended values according to spatial distribution of land use data provided by the Agricultural Census. The latter was based on the modelled N atmospheric deposition provided by the Review Group on Acid Rain (RGAR). Information on the extent of seminatural land was obtained from the Land Cover Map of Great Britain based on satellite data. The data sets were characterised by varied spatial resolution and were brought to a universal 5 km grid resolution prior to modelling emission as this was the best assumed resolution at the national scale. The predicted total of N20 emissions from British soils ranged between 128 and 140 kt N yl, and NO between 7 and 66 kt N y- 1, depending on the applied model. The predicted totals of N20 are higher than the estimates based on the approach of Bouwman (1995) and Skiba et al., (1996) using N emission factors. The higher NO emission rates based explicitly on the N factor suggest that the other approaches underestimate their totals (Simpson et al., 1999). The lower NO predicted in this study was due to the limiting effect of soil moisture. The different results of the models presented here are the result of the improved modelling approach used in this study, which takes into account the climatic characteristics of soils in addition to N input.The validation of the established models against field measurements from selected studies in Scotland showed their limited accuracy in predicting N20 and NO emissions at field scales. This was expected due to great spatial and temporal variability of emissions and the restricted methods of field measurements. While mechanistic models are better designed to reflect the emission processes at small scales, at national scales N20 and NO emissions are better predicted with simple regression models. This is mainly the result of limited availability of input data for large scale studies

Remote Sensing in Agriculture: State-of-the-Art

The Special Issue on “Remote Sensing in Agriculture: State-of-the-Art” gives an exhaustive overview of the ongoing remote sensing technology transfer into the agricultural sector. It consists of 10 high-quality papers focusing on a wide range of remote sensing models and techniques to forecast crop production and yield, to map agricultural landscape and to evaluate plant and soil biophysical features. Satellite, RPAS, and SAR data were involved. This preface describes shortly each contribution published in such Special Issue

Crop Disease Detection Using Remote Sensing Image Analysis

Pest and crop disease threats are often estimated by complex changes in crops and the applied agricultural practices that result mainly from the increasing food demand and climate change at global level. In an attempt to explore high-end and sustainable solutions for both pest and crop disease management, remote sensing technologies have been employed, taking advantages of possible changes deriving from relative alterations in the metabolic activity of infected crops which in turn are highly associated to crop spectral reflectance properties. Recent developments applied to high resolution data acquired with remote sensing tools, offer an additional tool which is the opportunity of mapping the infected field areas in the form of patchy land areas or those areas that are susceptible to diseases. This makes easier the discrimination between healthy and diseased crops, providing an additional tool to crop monitoring. The current book brings together recent research work comprising of innovative applications that involve novel remote sensing approaches and their applications oriented to crop disease detection. The book provides an in-depth view of the developments in remote sensing and explores its potential to assess health status in crops

Future of Sustainable Agriculture in Saline Environments

Food production on present and future saline soils deserves the world’s attention particularly because food security is a pressing issue, millions of hectares of degraded soils are available worldwide, freshwater is becoming increasingly scarce, and the global sea-level rise threatens food production in fertile coastal lowlands. Future of Sustainable Agriculture in Saline Environments aims to showcase the global potential of saline agriculture. The book covers the essential topics, such as policy and awareness, soil management, future crops, and genetic developments, all supplemented by case studies that show how this knowledge has been applied. It offers an overview of current research themes and practical cases focused on enhancing food production on saline lands. FEATURES Describes the critical role of the revitalization of salt-degraded lands in achieving sustainability in agriculture on a global scale Discusses practical solutions toward using drylands and delta areas threatened by salinity for sustainable food production Presents strategies for adaptation to climate change and sea-level rise through food production under saline conditions Addresses the diverse aspects of crop salt tolerance and microbiological associations Highlights the complex problem of salinity and waterlogging and safer management of poor-quality water, supplemented by case studies A PDF version of this book is available for free in Open Access at www.taylorfrancis.com. It has been made available under a Creative Commons Attribution-Non Commercial-No Derivatives 4.0 license

The Economics of Agricultural Non-Point Source Pollution Control: Investigating Synergies Between Spatial Targeting and Precision Agriculture

This thesis investigates the cost-effectiveness of agricultural non-point source pollution control policies through a biophysical-economic model for the Eden catchment in North-West England. Firstly, the presented thesis extensively reviews agri-environmental policy in the UK and the economic literature on non-point source pollution control. Moreover, in the context of current agricultural reforms in the UK and recent technological progress in agricultural technology, policy recommendations are drawn from a purpose-built biophysical-economic model covering six key non-point source pollutants (nitrogen and phosphorus to both the river and groundwater, sediment, and carbon emissions). The model is implemented in GAMS and characterised by a novel level of biophysical detail in the literature, including six farm types, six livestock types, 10 hydrological connectivity levels, five soil types, four slope types, 45 years of observed weather data, and 25 crops selected from 24 crop rotations. Policies are assessed over a range of abatement ambitions to facilitate evidence for different policymaker objectives. Overall, incentive-based fertiliser input taxes are found to be the most cost-effective policy mechanism in the Eden catchment. Notably, the presented results confirm previous findings in the literature of inelastic fertiliser demand. Consequently, high levels of taxation are required to achieve non-point source pollution abatement. Further, the novel assessment of Precision Agriculture in the context of a detailed catchment-scale biophysical-economic model highlights the necessary preconditions for precision agriculture to be cost-effectively implemented. Modelling of spatially targeted policies moreover highlights the synergies between spatial targeting and precision agriculture in this respect. Policymakers should ensure sufficient heterogeneity in biophysical variables (soil-types, slope-types, and hydrological connectivity levels) to safeguard successful applications of both spatial targeting and precision agricultur

Evaluating the sustainability of urban agriculture projects

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

Glastir Monitoring & Evaluation Programme. Second year annual report

What is the purpose of Glastir Monitoring and Evaluation Programme? Glastir is the main scheme by which the Welsh Government pays for environmental goods and services whilst the Glastir Monitoring and Evaluation Programme (GMEP) evaluates the scheme’s success. Commissioning of the monitoring programme in parallel with the launch of the Glastir scheme provides fast feedback and means payments can be modified to increase effectiveness. The Glastir scheme is jointly funded by the Welsh Government (through the Rural Development Plan) and the EU. GMEP will also support a wide range of other national and international reporting requirements. What is the GMEP approach? GMEP collects evidence for the 6 intended outcomes from the Glastir scheme which are focussed on climate change, water and soil quality, biodiversity, landscape, access and historic environment, woodland creation and management. Activities include; a national rolling monitoring programme of 1km squares; new analysis of long term data from other schemes combining with GMEP data where possible; modelling to estimate future outcomes so that adjustments can be made to maximise impact of payments; surveys to assess wider socio-economic benefits; and development of novel technologies to increase detection and efficiency of future assessments. How has GMEP progressed in this 2nd year? 90 GMEP squares were surveyed in Year 2 to add to the 60 completed in Year 1 resulting in 50% of the 300 GMEP survey squares now being completed. Squares will be revisited on a 4 year cycle providing evidence of change in response to Glastir and other pressures such as changing economics of the farm business, climate change and air pollution. This first survey cycle collects the baseline against which future changes will be assessed. This is important as GMEP work this year has demonstrated land coming into the scheme is different in some respects to land outside the scheme. Therefore, future analysis to detect impact of Glastir will be made both against the national backdrop from land outside the scheme and this baseline data from land in scheme. A wide range of analyses of longterm data has been completed for all Glastir Outcomes with the exception of landscape quality and historic features condition for which limited data is available. This has involved combining data with 2013/14 GMEP data when methods allow. Overall analysis of long term data indicates one of stability but with little evidence of improvement with the exception of headwater quality, greenhouse gas emissions and woodland area for which there has been improvement over the last 20 years. Some headline statistics include: 51% of historic features in excellent or sound condition; two thirds of public rights of way fully open and accessible; improvement in hedgerow management with 85% surveyed cut in the last 3 years but < 1% recently planted; 91% of streams had some level of modification but 60% retained good ecological quality; no change topsoil carbon content over last 25 years. What is innovative? GMEP has developed various new metrics to allow for more streamlined reporting in the future. For example a new Priority Bird species Index for Wales which combines data from 35 species indicates at least half have stable or increasing populations. The new GMEP Visual Quality Landscape Index has been tested involving over 2600 respondents. Results have demonstrated its value as an objective and repeatable method for quantifying change in visual landscape quality. A new unified peat map for Wales has been developed which has been passed to Glastir Contract Managers to improve targeting of payments when negotiating Glastir contracts. An estimate of peat soil contribution to current greenhouse gas emissions due to human modification has been calculated. Models have allowed quantification of land area helping to mitigate rainfall runoff. We are using new molecular tools to explore the effects of Glastir on soil organisms and satellite technologies to quantify e.g. small woody features and landcover change. Finally we are using a community approach to develop a consensus on how to define and report change in High Nature Value Farmland which will be reported in the Year 3 GMEP report

Mapping natural forest cover, tree species diversity and carbon stocks of a subtropical Afromontane forest using remote sensing.

Doctoral Degree. University of KwaZulu-Natal, Durban.Natural forests cover about a third of terrestrial landmass and provides benefits such as carbon sequestration, and regulation of biogeochemical cycles. It is essential that adequate information is available to support forest management. Remote Sensing imageries provide data for mapping natural forests. Hence, our study aimed at mapping the Nkandla Forest Reserve attributes with Remote Sensing imageries. Quantitative information on the forest attributes is non-existent for many of these forests, including the sub-tropical Afromontane Nkandla Forest Reserve. This does not support scientific and evidence based natural forest management. A review of literature revealed that progress has been made in Remote Sensing monitoring of natural forest attributes. The Random Forest (RF) and Support Vector Machine (SVM) were applied to Landsat 8 in classifying the land use land cover (LULC) classes of the forest. Each of the algorithms produced higher accuracy of above 95% with the SVM performing slightly better than the RF. The SVM, Markov Chain and Multi-Layer Perceptron Neural Network (MLPNN) were adopted for a spatiotemporal change detection over the last 30 years at decadal interval for the forest. There were consistent changes in each of the four LULC classes. The study further conducted a forecasting of LULC distribution for 2029. Aboveground carbon (AGC) estimation was carried out using Sentinel 2 imagery and RF modelling. Four models made up smade of Sentinel 2 products could successfully map the AGC with high accuracies. The last two studies focused on tree species diversity with the first evaluating the influence of spatial and spectral resolution on prediction accuracies by comparing the PlanetScope, RapidEye, Sentinel 2 and Landsat 8. Both the spatial and spectral resolution were found to influence accuracies with the Sentinel 2 emerging as the best imagery. The second aspect focused on identifying the best season for the prediction of tree species diversity. Summer imagery emerged as the best season and the winter being the least performer. Overall, our study indicates that Remote Sensing imageries could be used for successful mapping of natural forest attributes. The outputs of our studies could also be of interest to forest managers and Remote Sensing experts.Author's Publications and Manuscripts can be found on page iii