Tackling post-harvest cereal losses in sub-Saharan Africa

Post-harvest loss reduction raises food availability without increasing the use of land, water and agricultural inputs. This article refers to the case of grain to show the hurdles that farmers have to clear in taking measures to reduce losses and suggests ways that post-harvest practitioners can target mitigating actions in sub-Saharan Africa

Mapping the Spatial Distribution of Winter Crops at Sub-Pixel Level Using AVHRR NDVI Time Series and Neural Nets

For large areas, it is difficult to assess the spatial distribution and inter-annual variation of crop acreages through field surveys. Such information, however, is of great value for governments, land managers, planning authorities, commodity traders and environmental scientists. Time series of coarse resolution imagery offer the advantage of global coverage at low costs, and are therefore suitable for large-scale crop type mapping. Due to their coarse spatial resolution, however, the problem of mixed pixels has to be addressed. Traditional hard classification approaches cannot be applied because of sub-pixel heterogeneity. We evaluate neural networks as a modeling tool for sub-pixel crop acreage estimation. The proposed methodology is based on the assumption that different cover type proportions within coarse pixels prompt changes in time profiles of remotely sensed vegetation indices like the Normalized Difference Vegetation Index (NDVI). Neural networks can learn the relation between temporal NDVI signatures and the sought crop acreage information. This learning step permits a non-linear unmixing of the temporal information provided by coarse resolution satellite sensors. For assessing the feasibility and accuracy of the approach, a study region in central Italy (Tuscany) was selected. The task consisted of mapping the spatial distribution of winter crops abundances within 1 km AVHRR pixels between 1988 and 2001. Reference crop acreage information for network training and validation was derived from high resolution Thematic Mapper/Enhanced Thematic Mapper (TM/ETM+) images and official agricultural statistics. Encouraging results were obtained demonstrating the potential of the proposed approach. For example, the spatial distribution of winter crop acreage at sub-pixel level was mapped with a cross-validated coefficient of determination of 0.8 with respect to the reference information from high resolution imagery. For the eight years for which reference information was available, the root mean squared error (RMSE) of winter crop acreage at sub-pixel level was 10%. When combined with current and future sensors, such as MODIS and Sentinel-3, the unmixing of AVHRR data can help in the building of an extended time series of crop distributions and cropping patterns dating back to the 80s.JRC.H.4-Monitoring Agricultural Resource

Estimating Sub-pixel to Regional Winter Crop Areas using Neural Nets

The current work aimed at testing a methodology which can be applied to low spatial resolution satellite data to assess inter-annual crop area variations on sub-pixel to regional scales. The methodology is based on the assumption that within mixed pixels land cover variations are reflected by changes in the related hyper-temporal profiles of the Normalised Difference Vegetation Index (NDVI). We evaluated if changes in the fractional winter crop coverage are reflected in changing shapes of annual NDVI profiles and can be detected by using neural networks. The neural nets were trained on reference data obtained from high resolution Landsat TM/ETM images and additional ancillary data readily available (CORINE land cover). The proposed methodology was applied in a study region in central Italy to estimate winter crop areas between 1988 and 2002 from 1 km resolution NOAA-AVHRR profiles. The accuracy of the estimates was assessed by comparison to official agricultural statistics using a bootstrap approach. The method showed promise for estimating crop area variation on sub-pixel (cross-validated R2 between 0.7 and 0.8) to regional scales (normalized RMSE: 10%) and proved to have a significantly higher forecast capability than other methods used previously for the same study area.JRC.DG.G.3-Monitoring agricultural resource

Information for Meeting Africa’s Agricultural Transformation and Food Security Goals (IMAAFS)

The organizers of this international Conference on Information for Meeting Africa’s Agricultural Transformation and Food Security Goals (IMAAFS) included the African Union, the UN Economic Commission for Africa, and the European Commission (through the Joint Research Center). The Conference took place at the UN Conference Centre in Addis Ababa from 1 to 3 October 2014, to widen the availability and use of evidence-based information for agricultural growth and improved food and nutrition security. With over 180 international participants, the event brought together scientists and policy makers from a wide range of institutions and research organizations from Africa, Europe and the United States, as well as major UN agencies. The Conference took place over the course of three days and included nine presentation and discussion sessions (each with a chairperson and a rapporteur), executive morning briefs, break-out working groups, and a final decision-grid exercise to summarize the expert opinion of participants regarding the most promising strategies.JRC.H.4-Monitoring Agricultural Resource

APHLIS - Postharvest cereal losses in Sub-Saharan Africa, their estimation, assessment and reduction

APHLIS provides estimates of the postharvest weight losses (PHLs) of cereal grains for Sub-Saharan Africa. These loss estimates support agricultural policy formulation, identification of opportunities to improve value chains, improvement in food security (by improving the accuracy of cereal supply estimates), andmonitoring of loss reduction activities. APHLIS is based on a network of local experts (see Annex 1). Each country supplies and quality controls its own data that are stored in an exclusive area of a shared database. The APHLIS website displays the loss estimates as maps and tables. The APHLIS Network members also have the opportunity to post a ‘Country Narrative’ that gives a commentary on these postharvest losses in the context of the postharvest systems and projects of their countries. The loss estimates are generated by an algorithm (the PHL Calculator) that works on two data sets, the postharvest loss (PHL) profiles and the seasonal data. Each PHL profile is itself a set of figures, one for each link in the postharvest chain. These figures are derived from a very detailed search of the scientific literature followed by screening for suitability. They remain more or less constant between years. The seasonal data are contributed by the APHLIS Network and address several factors that are taken into account in the loss calculation. They may vary significantly from season to season and year to year. APHLIS estimates are not intended to be ‘statistics’ although they are computed using the best available evidence; they give an understanding of the scale of postharvest losses using a ‘transparent’ method of calculation. The estimates are assigned by primary administative unit (province) and may be aggregated to country or to region. Provinces are usually large geographical units and may include several agro-climatic zones, consequently the loss figures are generalisations, i.e. may be at variance from those experienced in particular situations. APHLIS recognises this limitation and offers a downloadable PHL Calculator that enables practitioners to change the default values to those that are specific to the situation of interest and to obtain loss estimates at a chosen geographical scale. The PHL Calculator can also be used with hypothetical data inorder to model ‘what if’ scenarios. APHLIS offers a robust system for the estimation of PHLs, is transparent in operation and can capture improvements in loss estimation over time by the accumulation of new and more accurate data. It encourages the collection of new data and offers advice on modern approaches to loss asssessment. For the future, APHLIS is envisaged as a much broader communcition hub that informs, motivates and coordinates efforts to optimise postharvest mangement

Using Low Resolution Satellite Imagery for Yield Prediction and Yield Anomaly Detection

Low resolution satellite imagery has been extensively used for crop monitoring and yield forecasting for over 30 years and plays an important role in a growing number of operational systems. The combination of their high temporal frequency with their extended geographical coverage generally associated with low costs per area unit makes these images a convenient choice at both national and regional scales. Several qualitative and quantitative approaches can be clearly distinguished, going from the use of low resolution satellite imagery as the main predictor of final crop yield to complex crop growth models where remote sensing-derived indicators play different roles, depending on the nature of the model and on the availability of data measured on the ground. Vegetation performance anomaly detection with low resolution images continues to be a fundamental component of early warning and drought monitoring systems at the regional scale. For applications at more detailed scales, the limitations created by the mixed nature of low resolution pixels are being progressively reduced by the higher resolution offered by new sensors, while the continuity of existing systems remains crucial for ensuring the availability of long time series as needed by the majority of the yield prediction methods used today.JRC.H.4-Monitoring Agricultural Resource

The 2015-2016 El Niño event: expected impact on food security and main response scenarios in East and Southern Africa

El Niño is a periodic climate phenomenon defined by anomalously warm sea surface temperatures in the eastern and central tropical Pacific Ocean which affect local weather worldwide and generally leads to increased drought risk at global level. In 2015, since July a strong El Niño is being observed with increasing intensity in September and October. It is expected to last for the first 3 months of 2016 and could reach a very high level of intensity in this period. To date, it has already affected climate in many parts of Asia and in the Northern parts of East Africa, causing serious rainfall deficits. Although the impact on agriculture is not directly proportional to the intensity of the climatic anomalies, the event is expected to impact East and Southern Africa in different ways. In East Africa, for the bimodal areas, El Niño events in the second half of the year usually lead to wetter than average conditions and are generally beneficial for agriculture. In other areas with a long crop season in the second half of the year, such as parts of Ethiopia, Sudan and Eritrea, it can cause drier than average conditions followed by rainfall at harvesting time causing drying problems. For both bimodal and single season zones it can lead to flooding in riverine areas and increase the risk of livestock diseases. On the contrary, in Southern Africa, strong El Niño events frequently cause drought and reduce crop production and this effect could be particularly dangerous considering the low crop production of this region in the 2014-2015 season. These risks need to be taken into consideration for response planning in East and Southern Africa and this report lists some main recommeJRC.H.4-Monitoring Agricultural Resource

Biomass estimation to support pasture management in Niger

Livestock plays a central economic role in Niger, but it is highly vulnerable due to the high inter-annual variability of rain and hence pasture production. This study aims to develop an approach for mapping pasture biomass production to support activities of the Niger Ministry of Livestock for effective pasture management. Our approach utilises the observed spatiotemporal variability of biomass production to build a predictive model based on ground and remote sensing data for the period 1998–2012. Measured biomass (63 sites) at the end of the growing season was used for the model parameterisation. The seasonal cumulative Fraction of Absorbed Photosynthetically Active Radiation (CFAPAR), calculated from 10-day image composites of SPOT-VEGETATION FAPAR, was computed as a phenology-tuned proxy of biomass production. A linear regression model was tested aggregating field data at different levels (global, department, agro-ecological zone, and intersection of agro-ecological and department units) and subjected to a cross validation (cv) by leaving one full year out. An increased complexity (i.e. spatial detail) of the model increased the estimation performances indicating the potential relevance of additional and spatially heterogeneous agro-ecological characteristics for the relationship between herbaceous biomass at the end of the season and CFAPAR. The model using the department aggregation yielded the best trade-off between model complexity and predictive power (R2 = 0.55, R2cv = 0.48). The proposed approach can be used to timely produce maps of estimated biomass at the end of the growing season before ground point measurements are made available.JRC.H.4-Monitoring Agricultural Resource

Mapping land enclosures and vegetation cover changes in the surroundings of Kenya's Dadaab refugee camps with very high resolution satellite imagery

First published: 04 November 2018 The immediate surroundings of refugee camps in drylands are among the areas exposed to highest pressure on natural resources including vegetation and soil. Understanding the dynamics of land fencing in these areas is critical for sustainable camp management and can help to improve the knowledge about land management in drylands in general. Very high resolution satellite imagery provides a means to observe such areas over time and to document land cover and use changes. This study uses satellite images to map fenced areas, which can be divided into pastoral enclosures and the so called 'green belts' (areas fenced for afforestation) around the Hagadera Camp in Dadaab (Kenya). It then analyses change dynamics between 2006 and 2013, a period where the refugee camp has been subject to high oscillations in camp population, due to a combination of conflicts and droughts in Somalia. The applied methodology allows detailed fence mapping and shows a large increase in fenced area (56%) over the 7-year period. Although new pastoral enclosures expanded into more densely vegetated surroundings, land cover density inside already fenced areas either decreased or remained stable. Green belt areas grew at a similar rate (58%) but did not show evidence of greening over time and their longer term success is strongly dependent on maintenance. The settlement area did also expand remarkably in the same time (65%), and human and animal movements in the surroundings intensified with a negative impact on vegetation density. The study could not fully investigate the socio-economic drivers and impacts linked to the rapid increase of enclosures, which are inextricably linked to evolutions in local agro-pastoral practices. However, by documenting spatial and temporal dynamics of fenced areas, it adds new evidence to their increasing relevance in rangeland management, and opens the way to a number of hypotheses, stimulating the debate about long-term ecological and socio-economic impact

Development of a national and sub-national crop calendars data set compatible with remote sensing derived land surface phenology

Crop calendars are a fundamental component of agricultural production monitoring systems since they help analysts to focus on the seasons when different crop types are actually growing in the field. The Earth Observation based early warning system ASAP (Anomaly hot Spots of Agricultural Production) uses land surface phenology (LSP) metrics as proxy for crop calendars and applies parameters, such as the start and end of the season (SOS and EOS respectively) to define the period of active agricultural vegetation growth at pixel level. However, such information is not crop specific and it remains therefore relevant to use crop calendars from independent sources providing crop specific key phenological timings, such as sowing, growing and harvesting. Several institutions, including FAO and USDA make available crop calendars at the national level, which are widely used for agricultural monitoring. The LSP derived SOS and EOS metrics can be associated with sowing and harvesting from such crop calendars. This report describes a method for the attribution of each growing season derived from LSP to a crop type listed in existing crop calendars. Based on a set of rules, we compare the growing seasons derived from LSP with the timings of the crop calendars, and select those crops where a match between LSP and crop calendar information is found. Agricultural statistics, including harvested area and production, are used in order to verify the correct identification and relevance of crop types by country. The method also allows to downscale the existing national level crop calendars to the sub-national level. It therefore makes available sub-national level crop calendars, which are highly valuable for crop monitoring at that scale. The resulting crop calendars are available in the ASAP download section: https://mars.jrc.ec.europa.eu/asap/download.phpJRC.D.5-Food Securit