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Highlights • Hotspots of drought, cold, iron toxicity salinity/sodicity stress occurrence for rice in Africa. • Maps for targeted distribution of tolerant varieties. • Drought most important stress (33% of rice area) then iron toxicity (12%). • Risk of cold/salinity/sodicity in 7–2% of Africa’s rice area., Maps of abiotic stresses for rice can be useful for (1) prioritizing research and (2) identifying stress hotspots, for directing technologies and varieties to those areas where they are most needed. Large-scale maps of stresses are not available for Africa. This paper considers four abiotic stresses relevant for rice in Africa (drought, cold, iron toxicity and salinity/sodicity). Maps showing hotspots of the stresses, the countries most affected and total potentially affected area are presented. In terms of relative importance, the study identified drought as the most important stress (33% of rice area potentially affected), followed by iron toxicity (12%) and then cold (7%) and salinity/sodicity (2%). Hotspots for iron toxicity, cold and salinity are identified. For drought, local variation along the hydromorphic zone was a stronger determinant than larger-scale climatic variation, therefore mapping of drought based on climatic zones has only limited value. Uncertainties in the mappings are discussed.

Currently, many precision agriculture recommendations are based on empirical models, for example when a nitrogen application rate is calculated from a drone or satellite image. In the future such recommendations will also use mechanistic models of crop growth. It is a challenge to calibrate these models to the specific conditions of each farm and each farm field, especially so for soil parameters. The aim of our work was to operationalize the estimation of soil parameters via inverse modelling, using remote sensing and yield monitor data. We calibrated the WOFOST model using 5 years of data from 10 commercial fields in The Netherlands on which potato was the main crop and where sugar beet, maize and wheat were grown in non-potato years. Starting values for soil parameters were taken from the national soil map. Model outcome proved especially sensitive to field capacity and depth of the soil. Inverse modelling resulted in improved accuracy of simulated LAI and crop yield. We report the increase in accuracy of the simulations and the change in soil parameters relative to their starting values.

The accuracy of models to predict the impact of changing climate factors on crop growth is influenced by data availability and quality, model structure, and model calibration. In this context, we previously evaluated the ability of an ensemble of ten potato crop models to simulate the effect of ambient (aC) and elevated (eC) atmospheric carbon dioxide concentration on yield at 8 experimental locations across Europe. Each modeling group developed a single cross-location calibration parameter set using aC data from just two locations. Simulations were then conducted using this ‘limited’ calibration across all locations for aC and eC responses. Results indicated that the mean of all model responses were within the range of observed variation when averaged across all locations, but this accuracy varied substantially with experimental site. In the next phase of this study, modelers developed site-specific calibration parameters for each individual location, as well as one single cross-location calibration set, using the full set of aC data. This was viewed as a ‘full’ calibration approach since data from all 8 experiments were made available to the modelers. Model simulations were then conducted for eC response at each location using these new within- and cross-location calibration parameter sets. This presentation will focus on a) the differences in the accuracy of model response to eC across all sites using within- versus cross-location calibration, b) the variation in predicted yields among individual locations, and c) comparison of cross-, and within-, location responses to eC and aC between the ‘full’ and ‘limited’ calibration approaches. Multi-model accuracy to aC and eC responses, and the geospatial stability of the different model calibration parameter sets, will be quantified. Insights regarding the influence that data availability and calibration methodology have on these results will also be assessed.

By the year 2050, the world’s population will need 60% more food than it did in 2005. In sub-Saharan Africa (we’ll call it SSA) (Fig. 1) this problem will be even greater, with the demand for cereals increasing by more than three times as the population rises.We collected and calculated farming data for 10 countries in sub-Saharan Africa. This made us realize that countries in SSA must make many large changes to ncrease their yield of cereals (the amount of cereals that are grown on the current farmland each year) to meet this greater demand.If countries in SSA are unable to increase cereal yield, there are two options. either farmland areas will have to increase drastically, at the expense of natural land, or SSA will need to buy more cereal from other countries than it does today. This may put more people in these countries at risk of not having enough food to be able to live healthily.

Yield gap analysis has become popular to assess how much and where food production can be increased on existing land. It is also helpful in identifying an acceptable compromise between yield, resource use efficiency and local emissions of nutrients or crop protection agents, as resource use efficiencies tend to decrease once yields exceed a certain percentage of potential yields (e.g. 80%). The literature provides many examples of global and regional studies with yield gap analyses. The global ones are appealing because of their consistent use of one method and global databases, but they lack local or even regional agronomic rigour. Regional studies use a range of different methodologies and are therefore hard to compare mutually. In the Global Yield Gap Atlas (www.yieldgap.org) yield gaps of all key agricultural commodities are estimated for all food producing countries, using a global protocol. The protocol is always applied with local data on weather, soils, cropping systems and actual farm yields, and the results are evaluated with local experts. This paper presents results for an initial 35 countries covering, respectively, c.60%, 58%, and 35% of global rice, maize, and wheat production. It then demonstrates how results can be used to explore options for future self-sufficiency in cereal production in sub-Saharan Africa, the sub-continent with the fastest increase in cereal demand until 2050. Next, the paper presents a method that enables yield gap analysis to be used for the prioritisation of research and development investments. Once yield gaps have been assessed, a key follow up question is why yield gaps exist: what are their underlying biophysical and socio-economic causes? To this end it is helpful to decompose yield gaps into efficiency, resource and technology gaps. Finally, yield gaps can be usefully translated into nutrient (uptake and application) gaps. These indicate by how much the balanced nutrition of crops should increase to realise a certain percentage of yield gap closure.

More and more irrigated rice farms of the Senegal River Valley (SRV) no longer respect the sowing periods promoted in the 90s to reduce sterility risks due to extreme temperatures. This study aims at understanding that reality and assess ing whether new sowing periods must be defined. Combining focus - groups and surveys, climate analysis, field experiments and modeling work with RIDEV model, it addresses the evolution of cropping practices and their constraints, farmers ' climate perception, climate evolution and its consequences on rice development and sowing periods in the SRV. Data analysis shows rainfalls and temperature increases, and particularly a significant increase between the present decade and the 1950 - 1980 per iod which was considered for the establishment of the recommended sowing windows (+1°C to +2°C on monthly averages for Podor), with less extreme cold temperatures and more extreme hot ones. Farmers are very aware about recent climate evolution, with respec tively 94% and 72% of them saying that rainfall and temperature patterns have changed. More precisely they commented that “the cold period shifted by about one month, from “October/November – February/March” to “November – March/April”. Nevertheless the ma jority considers that the recommended sowing periods are still pertinent and explain that late sowings are due to delayed access to tractors, inputs and credits. Only few ones (5 %) intentionally sow late, considering there is no longer a danger in doing t hat. However, in 2011 farmers who sown later got very bad yield and farmers explain that “because the cold arrived earlier as it happened in the past”. Yet, while farmer's comments appear coherent with climate data, up to now we can't totally confirm them by crop modelling since we still have difficulties in the simulation of the sterility despite recent model improvements. Additional work is required to reach a conclusion .

In the 1990s, AfricaRice developed RIDEV crop model to assess irrigated rice phenology and extreme temperature impacts on yields, and defined sowing windows for irrigated rice in Senegal River valley (SRV) and Niger River valley (NRV) that minimized climate risks. Based on recent observations, extension agents comment that "more and more farmers sow out of the recommended sowing windows and get good yield. It seems that the climate changed". Those observations raise the following questions: Did farmers' practices change? If so, why? Is there any relationship with climate or other factors? Did the climate really change? Are recommended sowing windows still valid? To address these issues, AfricaRice, SAED, IER, Hohenheim University, Office du Niger and CIRAD launched an integrative project funded by Agence National de la Recherche (ANR) Changement Environnementaux et Socio en Afrique: Passé, Présent et Futur (ESCAPE) and Climate Change, Agriculture and Food Security (CCAFS, CGIAR Research Program) projects, the final objective of which is to define the most adapted sowing windows for recommended irrigated rice varieties in SRV and NRV in Mali in order to reduce climate risks and maximize production. We will further assess the `residual' risks related to those sowing windows in order to develop insurance tools, and to evaluate future situations using climatic scenarios. To achieve this, we started different research activities: (a) - Focus groups and surveys to study farmers' practices, their evolution, constraints and driving factors, and farmers' perception of climate and its evolution in last years. (b) - Agronomic trials, grain quality and biotechnological analysis to verify whether genetic drift has occurred on the main cropped variety, Sahel 108, in the last 20 years that could explain changes in crop phenology and behavior. Thirty Sahel 108 lines/sources have been compared: 2 original Sahel 108 sources from IRRI, 2 sets of breeder seed from ISRA, 19 sets of foundation seed from seed producers and 7 sources from producers having used their own seeds several times (from 2 to 21 years). (c) - Analysis of daily climatic data of the last 30 years. (d) - Agronomic surveys and trials and crop-modeling work aimed at improving and/or validating crop models. These activities started in 2008 within Developing rice and sorghum crop adaptation strategies for climate change in vulnerable environments in Africa (RISOCAS) project and have been carried out by different research teams. Agronomic surveys and trials provided data about rice phenology, yields and yield components, and micro-climate (relationship between water and air temperatures, panicle temperatures). RIDEV and Samara models have been improved and will be validated for local varieties and situations. (e) - Agro-climatological analysis using the validated models results aimed at assessing impacts of climate on crop growth during the last 30 years will be compared to surveys information, defining recommended sowing periods and associated residual climatic risks in order to develop insurance tools, and exploring future situations based on climatic scenarios. The modeling tools will be transferred to NARS and extension institutes (SAED, Office Niger, ISRA and IER) for better management of crop calendars and climate risk assessments.

For some years it has been observed that many rice farmers in the Senegal River valley (SRV) no longer respect the recommendations of sowing periods established by AfricaRice in the early 1990s to reduce the risks due to extreme cold and hot temperatures. Moreover, some farmers seem to get very good yields sowing out of the recommended sowing windows. A collaborative AfricaRice-SAED-CIRAD study started in 2012 within both Agence National de la Recherche (ANR) Changement Environnementaux et Socio en Afrique: Passe, Présent et Future (ESCAPE) and Climate Change, Agriculture and Food Security (CCAFS, CGIAR Research Program) projects aimed at analyzing this reality and understanding the main determinants of the shift in farmers' practices. The questions addressed included: (a) the current cropping practices and their constraints, and their possible evolution; (b) the perception of climate and its possible evolution by farmers; (c) analysis of the climate of last 30 years; (d) analysis of the consequences of climate evolution on rice development and rice cropping systems; and (e) update of the recommended sowing periods according to different rice varieties in order to minimize climate risks in the valley. The analysis of cropping practices and perceptions of climate by farmers started with 11 focus groups held throughout the SRV. The focus group was followed by individual surveys to get quantified information. An analysis of historical temperature was also started, along with modeling work (using RIDEV, Samara or Oryza) in order to assess the consequences of the climate on rice development in the SRV and to update knowledge of climate risks for different sowing dates for different rice varieties. Additional studies were also launched to verify eventual genetic drift of main cropped variety and to get data on rice behavior and phenology in farmers' fields. The main results achieved so far are presented. Focus groups indicated that farmers are very aware about climate and its recent evolution during the last decade. According to them, the rain is more important and poorly distributed in recent years, with the beginning and end of the seasons more unpredictable. It is also warmer during the hot period and colder during cold period, and the cold period has shifted and extended. Some farmers also explained reductions in yields by evolutions of both hot and cold temperatures. The shift of the cold period explains why some farmers have changed their cropping practices. However, in 2011 farmers who had sown later got bad yield because the cold arrived earlier, as it did in the past. We believe that must be related to the 2011 rainy season, which was drier than recent years but comparable to what it was in the 1990s. Detailed climate analysis is underway and will be presented at the Congress. We would like to finely characterize the evolution of temperatures and also to analyze the relationship between the rainfall amount and the cold temperatures pattern. Observations will be compared to results obtained using crop model.

The AgriAdapt project has developed methodologies that enable (a) the assessment of impacts, risks and resiliencies for agriculture under changes in climatic conditions but also under changes of other drivers (market, technology, policy, etc.) and (b) the evaluation of adaptation strategies at farm type and regional scale. The methodologies are applied to arable farming over Europe and in a more integrated way, to that in Flevoland, the Netherlands as the key case. The methodologies at European level include (a) Crop modelling and (b) Market modelling. The methodologies at regional level cover the following main areas: (a) Integrated sustainability assessment, (b) Development of scenarios of farm structural change towards 2050, (c) Calculation of crop yields for different scenarios in 2050 inclusive agro-climate calendars, and (d) Partial and fully integrated analysis of farming systems in 2050, inclusive the aggregation to the regional level. Results from the application of the different methodologies are presented here. For example, exploring future farming systems shows that the most important driving factors towards 2050 within the A1-W scenario with a globalized economy, are (a) the yield increase due to climate change, (b) the expected product price change and (c) the degree of innovation in crop productivity. The effects of climate change are projected to have a positive economic effect on arable farming.

A key objective of the AgriAdapt project is to assess climate change impacts on agriculture including adaptation at regional and farm type level in combination with market and technological changes. More specifically, the developed methodologies enable (a) the assessment of impacts, risks and resiliencies for agriculture under changes in climatic conditions including increasing climate variability but also under changes of other drivers (market, technology, policy, etc.) and (b) the evaluation of adaptation strategies at farm type and regional scale. The methodologies are applied to arable farming in Flevoland, the Netherlands as the key case.

A key objective of the AgriAdapt project is the development of methodologies to assess climatic change impacts on agriculture including adaptation at regional and farm type level in combination with market changes. More specifically, the methodologies should enable (a) the assessment of impacts, risks and resiliencies for agriculture under changes in climatic conditions including increasing climate variability but also under changes of other drivers (market, technology, policy, etc.) and (b) the evaluation of adaptation strategies at farm type and regional scale.

Rice is the world's most important staple food. Although mainly produced in Asia (91%), it is consumed on all continents and its global importance and consumption is increasing. The limited scope to expand production areas coupled with increasing resource constraints (mainly the lack of or competing demands for land and water) make it difficult to meet necessary production increases. Climate change in terms of increasing temperatures, more frequent droughts, anticipated loss of productive estuaries due to rising sea levels, more frequent and severe storms and rising CO2 levels further compounds these problems. This constitutes a huge challenge for science, policy and farmers. The provision of effective solutions is complex due to the spatialtemporal dimensions that must be integrated when setting research, policy and management priorities. These challenges have motivated us to form a Community of Practice (CoP) of concerned scientists. We formed this CoP around the central theme of simulation modelling as a technology that allows integration of discipline-based component science across space and time. We also use modelling as an engagement tool with stakeholders and to connect seemingly disparate scientific disciplines. Here we put our Research for Development (R4D) activities into context and report on some of the research efforts that our CoP is currently involved in. In our quest to design locally-adapted, profitable and sustainable, climate-robust rice-based cropping systems, we welcome input from the wider, global R4D community.

Hoe verkrijg je een overzicht van het aanbod aan datasets en hoe maak je de beste keuze. Een geoportaal kan meerwaarde bieden ten opzichte van de andere kanalen

The growing availability of spatial data along with growing ease to use the spatial data (thanks to wide-scale adoption of GIS) have made it possible to use spatial data in applications inappropriate considering the quality of the data. As a result, concerns about spatial data quality have increased. To deal with these concerns, it is necessary to (1) formalise and standardise descriptions of spatial data quality and (2) to apply these descriptions in assessing the suitability (fitness for use) of spatial data, before using the data. The aim of this thesis was twofold: (1) to enhance the description of spatial data quality and (2) to improve our understanding of the implications of spatial data quality.Chapter 1 sets the scene with a discussion on uncertainty and an explanation of why concerns about spatial data quality exist. Knowledge gaps are identified and the chapter concludes with six research questions.Chapter 2 presents an overview of definitions of spatial data quality. Overall, I found a strong agreement on which elements together define spatial data quality. Definitions appear to differ in two aspects: (1) the location within the meta-data report: some elements occur not in the spatial data quality section but in another section of the meta-data report; and (2) the explicitness with which elements are recognised as individual elements. For example, the European pre-standard explicitly recognises theelement'homogeneity'. Other standards recognise the importance of documenting the variation in quality, without naming it explicitly as an individual element.In chapter 3 we quantified the spatial variability in classification accuracy for the agricultural crops in the Dutch national land cover database (LGN). Classification accuracy was significantly correlated with: (1) the crop present according to LGN, (2) the homogeneity of the 8-cell neighbourhood around each cell, (3) the size of the patch in which a cell is located, and (4) the heterogeneity of the landscape in which a cell is located.In chapter 4 I present methods that use error matrices and change detection error matrices as input to make more accurate land cover change estimates. It was shown that temporal correlation in classification errors has a significant impact and must be taken into account. Producers of time series land cover data are recommended not only to report error matrices, but also change detection error matrices.Chapter 5 focuses on positional accuracy and area estimates. From the positional accuracy of vertices delineating polygons, the variance and covariance in area can be derived. Earlier studies derived equations for thevariance,this chapter presents a covariance equation. The variance and covariance equation were implemented in a model and applied in a case-study. The case-study consisted of 97 polygons with a small subsidy value (in euros per hectare) assigned to each polygon. With the model we could calculate the uncertainty in the total subsidy value (in euros) of the complete set of polygons as a consequence of uncertainty in the position of vertices.Chapter 6 explores the relationship between completeness of spatial data and risk in digging activities around underground cables and pipelines. A model is presented for calculating the economic implications of over- and incompleteness. An important element of this model is therelationship between detection time and costs. The model can be used to calculate the optimal detection time, i.e. the time at which expected costs are at their minimum.Chapter 7 addresses the question why risk analysis (RA) is so rarely applied to assess the suitability of spatial data prior to using the data. In theory, the use of RA is beneficial because it allows the user to judge if the use of certain spatial data does not produce unacceptable risks. Frequently proposed hypotheses explaining the scarce adoption of RA are all technical and educational. In chapter 7 we propose a new group of hypotheses, based on decision theory. We found that the willingness to spend resources on RA depends (1) on the presence of feedback mechanisms in the decision-making process, (2) on how much is at stake and (3) to a minor extent on how well the decision-making process can be modelled.Chapter 8 presents conclusions on the six research questions (chapters 2-7) and lists recommendations for users, producers and researchers of spatial data. With regard to the description, four recommendations are given. Firstly, spend more effort on documenting the lineage of reference data. Secondly, quantify and report correlation of quality between related data sets. Thirdly, investigate the integration of different forms of uncertainty (error, vagueness, ambiguity). Fourthly, study the implementation and use of spatial data quality standards. With regard to the application of spatial data quality descriptions, I have two main recommendations. Firstly, to continue the line of research followed in this thesis: quantification of implications of spatial data quality, through development of theory along with tangible illustrations in case-studies. Secondly, there is a need for more empirical research into how users cope with spatial data quality.

Graafschade is schade aan ondergrondse kabels en leidingen als gevolg van graafwerkzaamheden. Een nieuw wetsvoorstel beoogt deze schade te beperken. In een promotie-onderzoek stelde de auteur de gevolgen van kwaliteit van geo-informatie aan de orde. Dit artikel geeft een algemeen overzicht bij dit wetsvoorstel: in een achttal vragen komem zowel de problematiek als de gevolgen aan de orde

Risk analysis (RA) has been proposed as a means of assessing fitness for use of spatial data but is only rarely adopted. The proposal is that better decisions can be made by accounting for risks due to errors in spatial data. Why is RA so rarely adopted? Most geographical information science (GISc) literature stresses educational and technical constraints. In this article we propose, based on decision theory, a number of hypotheses for why the user would be more or less willing to spend resources on RA. The hypotheses were tested with a questionnaire, which showed that the willingness to spend resources on RA depends on the presence of feedback mechanisms in the decision-making process, on how much is at stake, and to a minor extent on how well the decision-making process can be modeled.