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See detailRemotely-sensed assessment of the impact of century-old biochar on chicory crop growth using high-resolution UAV-based imagery
Heidarian Dehkordi, Ramin ULiege; Denis, Antoine ULiege; Fouche, Julien et al

in International Journal of Applied Earth Observation and Geoinformation (2020), 91(September 2020),

In recent years, special attention has been given to the long-term effects of biochar on the performance of agro-ecosystems owing to its potential for improving soil fertility, harvested crop yields, and ... [more ▼]

In recent years, special attention has been given to the long-term effects of biochar on the performance of agro-ecosystems owing to its potential for improving soil fertility, harvested crop yields, and aboveground biomassproduction. The present experiment was set up to identify the effects on soil-plant systems of biochar producedmore than 150 years ago in charcoal mound kiln sites in Wallonia (Belgium). Although the impacts of biochar onsoil-plant systems are being increasingly discussed, a detailed monitoring of the crop dynamics throughout thegrowing season has not yet been well addressed. At present there is considerable interest in applying remotesensing for crop growth monitoring in order to improve sustainable agricultural practices. However, studiesusing high-resolution remote sensing data to focus on century-old biochar effects are not yet available. For thefirst time, the impacts of century-old biochar on crop growth were investigated at canopy level using high-resolution airborne remote sensing data over a cultivatedfield. High-resolution RGB, multispectral and thermalsensors mounted on unmanned aerial vehicles (UAVs) were used to generate high frequency remote sensinginformation on the crop dynamics. UAVs wereflown over 11 century-old charcoal-enriched soil patches and theadjacent reference soils of a chicoryfield. We retrieved crucial crop parameters such as canopy cover, vegetationindices and crop water stress from the UAV imageries. In addition, our study also providesin-situmeasurementsof soil properties and crop traits. Both UAV-based RGB imagery andin-situmeasurements demonstrated that thepresence of century-old biochar significantly improved chicory canopy cover, with greater leaf lengths in biocharpatches. Weighted difference vegetation index imagery showed a negative influence of biochar presence on plantgreenness at the end of the growing season. Chicory crop stress was significantly increased by biochar presence,whereas the harvested crop yield was not affected. The main significant variations observed between referenceand century-old biochar patches usingin situmeasurements of crop traits concerned leaf length. Hence, theoutput from the present study will be of great interest to help developing climate-smart agriculture practicesallowing for adaptation and mitigation to climate. [less ▲]

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See detailSimulating and analysing climate change impacts on crop yields in Morocco using the CARAIB dynamic vegetation model driven by Med- CORDEX projections
Loudiyi, Iliass ULiege; Jacquemin, Ingrid ULiege; Tychon, Bernard ULiege et al

Conference (2020, May 08)

Morocco, by its geographical position and its climate, is strongly affected by climate change and presents an ever-increasing vulnerability. In fact, the country's economy, being very dependent on ... [more ▼]

Morocco, by its geographical position and its climate, is strongly affected by climate change and presents an ever-increasing vulnerability. In fact, the country's economy, being very dependent on agriculture, would be greatly affected. It is therefore necessary to further develop knowledge about climate change and strengthen forcasting systems for predicting the impacts of climate change. The agriculture in Morocco is largely dominated by rainfed crops and therefore dependent on pluviometry. According to the Global Yield Gap Atlas, about 43% of arable land is devoted to cereals, 7% to plantation crops (olives, almonds, citrus, grapes, dates), 3% to pulses, 2% to forage, 2% to vegetables, 2% to industrial crops (sugar beets, sugar cane, cotton) and oilseeds, and 42% is fallow. In this project we are going to focus on cereals, olives, potatoes and sugar beets. Regarding the climate, Morocco is characterized by a wide variety of topographies ranging from mountains to plains, oasis and Saharan dunes. For this reason, the country experiences diverse climatic conditions with large spatial and intra- and inter-annual variability of precipitation. Morocco faces irregular rain patterns, cold spells and heat waves increasingly resulting in droughts, which significantly affects agriculture. Our research, funded by a bilateral project of Wallonie-Bruxelles International, aims to study the response of Moroccan agriculture to climate change, using the dynamic vegetation model CARAIB (CARbon Assimilation In the Biosphere) developed within the Unit for Modelling of Climate and Biogeochemical Cycles (UMCCB) of the University of Liège. This spatial model includes crops and natural vegetation and may react dynamically to land use changes. Originally constructed to study vegetation dynamics and carbon cycle, it includes coupled hydrological, biogeochemical, biogeographical and fire modules. These modules respectively describe the exchange of water between the atmosphere, the soil and the vegetation, the photosynthetic production and the evolution of carbon stocks and fluxes in this vegetation-soil system. The biogeographical module describes, for natural vegetation, the establishment, growth, competition, mortality, and regeneration of plant species, as well as the occurrence and propagation of fires. For crops, a specific module describes basic management (sowing, harvest, rotation) and phenological phases. Model simulations are performed across north-west Morocco, where the crops activities are important, by using different input data. The timeline of simulations is divided in two periods: past (from 1901 to 2018[LF1] ) and future (from 2019 to 2100). For the past period, we are using high resolution (30 arc sec) gridded climate data derived from WorldClim (climatology) and interpolated anomalies from Climate Research Unit CRU (trend and variability). For the future period, we use interpolated and bias-corrected fields from a regional climate model (ALADIN-Climate) from the Med-CORDEX initiative run at a spatial resolution of 12 km and for three different Representative Concentration Pathway scenarios (RCP2.6, RCP4.5 and RCP8.5). [less ▲]

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See detailInternational Research in Environment, Geography and Earth Science Vol.1
Sossey Alaoui, Khadija ULiege; Tychon, Bernard ULiege; Rosillon, Francis ULiege

in Sossey Alaoui, Khadija (Ed.) International Research in Environment, Geography and Earth Science Vol.1 (2020)

This book covers all areas of Geography, Environment and Earth Science. The scope of the book is outlined below. Geography related book chapters are expected to cover: Cartography‎, Human geography‎ ... [more ▼]

This book covers all areas of Geography, Environment and Earth Science. The scope of the book is outlined below. Geography related book chapters are expected to cover: Cartography‎, Human geography‎, Physical geography‎, Political geography‎, Regional geography, Animal geography, etc. Environmental science related book chapters are expected to cover: Interdisciplinary research that integrates physical, biological and information sciences (including ecology, biology, physics, chemistry, plant science, zoology, mineralogy, oceanography, limnology, soil science, geology and physical geography, and atmospheric science) to the study of the environment, and the solution of environmental problems. Earth science related book chapters are expected to cover: Research and study of all fields of natural science related to the planet Earth, dealing with the physical constitution of the Earth and its atmosphere. This book contains various materials suitable for students, researchers and academicians and expected to be very useful. [less ▲]

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See detailA study on trade-offs between spatial resolution and temporal sampling density for wheat yield estimation using both thermal and calendar time
Durgun, Yetkin Özüm ULiege; Gobin, Anne; Duveiller, Grégory et al

in International Journal of Applied Earth Observation and Geoinformation (2020), 86

Within-season forecasting of crop yields is of great economic, geo-strategic and humanitarian interest. Satellite Earth Observation now constitutes a valuable and innovative way to provide spatio-temporal ... [more ▼]

Within-season forecasting of crop yields is of great economic, geo-strategic and humanitarian interest. Satellite Earth Observation now constitutes a valuable and innovative way to provide spatio-temporal information to assist such yield forecasts. This study explores different configurations of remote sensing time series to estimate of winter wheat yield using either spatially finer but temporally sparser time series (5daily at 100 m spatial resolution) or spatially coarser but denser (300 m and 1 km at daily frequency) time series. Furthermore, we hypothesised that better yield estimations could be made using thermal time, which is closer to the crop physiological development. Time series of NDVI from the PROBA-V instrument, which has delivered images at a spatial resolution of 100 m, 300 m and 1 km since 2013, were extracted for 39fields for field and 56fields for regional level analysis across Northern France during the growing season 2014-2015. An asymmetric double sigmoid model was fitted on the NDVI series of the central pixel of the field. The fitted model was subsequently integrated either over thermal time or over calendar time, using different baseline NDVI thresholds to mark the start and end of the cropping season. These integrated values were used as a predictor for yield using a simple linear regression and yield observations at field level. The dependency of this relationship on the spatial pixel purity was analysed for the 100 m, 300 m and 1 km spatial resolution. At field level, depending on the spatial resolution and the NDVI threshold, the adjustedR²ranged from 0.20 to 0.74; jackknifed–leave-one-field-outcross validation–RMSE ranged from 0.6 to 1.07 t/ha and MAE ranged between 0.46 and 0.90 t/ha for thermal time analysis. The best results for yield estimation (adjustedR²= 0.74, RMSE =0.6 t/ha and MAE =0.46 t/ha)were obtained from the integration over thermal time of 100 m pixel resolution using a baseline NDVI threshold of 0.2 and without any selection based on pixel purity. The field scale yield estimation was aggregated to the regional scale using 56fields. At the regional level, there was a difference of 0.0012 t/ha between thermal and calendar time for average yield estimations. The standard error of mean results showed that the error was larger for a higher spatial resolution with no pixel purity and smaller when purity increased. These results suggest that, for winter wheat, a finer spatial resolution rather than a higher revisit frequency and an increasing pixel purity enable more accurate yield estimations when integrated over thermal time at the field scale and at the regional scale only if higher pixel purity levels are considered. This method can be extended to larger regions, other crops, and other regions in the world, although site and crop-specific adjustments will have to include other threshold temperatures to reflect the boundaries of phenological activity. In general, however, this methodological approach should be applicable to yield estimation at the parcel and regional scales across the world. [less ▲]

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See detailWeather-Based Predictive Modeling of Wheat Stripe Rust Infection in Morocco
El Jarroudi, Moussa ULiege; Lahlali, Rachid; Kouadio, Louis et al

in Agronomy (2020), 10(2), 1-18

Predicting infections by Puccinia striiformis f. sp. tritici, with su cient lead times, helps determine whether fungicide sprays should be applied in order to prevent the risk of wheat stripe rust (WSR ... [more ▼]

Predicting infections by Puccinia striiformis f. sp. tritici, with su cient lead times, helps determine whether fungicide sprays should be applied in order to prevent the risk of wheat stripe rust (WSR) epidemics that might otherwise lead to yield loss. Despite the increasing threat of WSR to wheat production in Morocco, a model for predicting WSR infection events has yet to be developed. In this study, data collected during two consecutive cropping seasons in 2018–2019 in bread and durum wheat fields at nine representative sites (98 and 99 fields in 2018 and 2019, respectively) were used to develop a weather-based model for predicting infections by P. striiformis. Varying levels of WSR incidence and severity were observed according to the site, year, and wheat species. A combined e ect of relative humidity > 90%, rainfall 0.1 mm, and temperature ranging from 8 to 16 C for a minimum of 4 continuous hours (with the week having these conditions for 5% to 10% of the time) during March–May were optimum to the development of WSR epidemics. Using the weather-based model, WSR infections were satisfactorily predicted, with probabilities of detection 0.92, critical success index ranging from 0.68 to 0.87, and false alarm ratio ranging from 0.10 to 0.32. Our findings could serve as a basis for developing a decision support tool for guiding on-farm WSR disease management, which could help ensure a sustainable and environmentally friendly wheat production in Morocco. [less ▲]

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See detailEmploying Weather-Based Disease and Machine Learning Techniques for Optimal Control of Septoria Leaf Blotch and Stripe Rust in Wheat
El Jarroudi, Moussa ULiege; Lahlali, Rachid; El Jarroudi, Haifa et al

in Advanced Intelligent Systems for Sustainable Development (2020)

Septoria tritici blotch (STB) is among the most important crop diseases causing continuous threats to wheat production worldwide. STB epidemics are the outcome of interactions between susceptible host ... [more ▼]

Septoria tritici blotch (STB) is among the most important crop diseases causing continuous threats to wheat production worldwide. STB epidemics are the outcome of interactions between susceptible host cultivars, favorable environmental conditions, and sufficient quantities of pathogen inoculum. Thus, to determine whether fungicide sprays should be applied to prevent the risk of epidemics that might otherwise lead to yield loss, weatherbased systems as stand-alone or combined with other disease or agronomic variables have been implemented in decision-support systems (DSS). Given the economic importance of wheat in Morocco and increasing concerns caused by fungal plant pathogens in wheat-growing regions, DSS integrating a disease risk model would help to limit potentially harmful side effects of fungicide applications while ensuring economic benefits. Here we describe the use of an artificial intelligence algorithm, i.e. the artificial neural network, within a weatherbased modelling approach to predict the progress of STB in wheat in Luxembourg. The reproducibility of area-specific modelling approaches is often a hurdle for their application in operational disease warning system at a regional scale. Hence, we explore the potential of coupling artificial intelligence algorithms with weather-based model for predicting in-season progress of a major economically important fungal disease – wheat stripe rust – in selected wheatproducing regions in Morocco. [less ▲]

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See detailManaging the high variability of compressed sward heights to model grass growth on pastures using satellite images
Nickmilder, Charles ULiege; Soyeurt, Hélène ULiege; Dufrasne, Isabelle ULiege et al

Poster (2020, January 31)

ROADSTEP is a Walloon research program aiming to develop decision tools to help farmers in their daily herd monitoring on pastures. One of the aims is to develop a modelling tool to predict the ... [more ▼]

ROADSTEP is a Walloon research program aiming to develop decision tools to help farmers in their daily herd monitoring on pastures. One of the aims is to develop a modelling tool to predict the availability of pasture feeding based on satellite images, meteorological variables and soil characteristics. So, 72,975 compressed sward heights (CSH) have been measured on 30 parcels located in 3 farms using Jenquip EC20G platemeter in 2018 and 2019. CSH records (175 ± 53 mm) seemed to be normally distributed based on the low values of skewness (-1.96) and kurtosis (3.28). However, CSH gathered per parcel and per date showed a trend to unfit a normal distribution and seemed to be dependent on the location of the measurement spot on the parcel. Indeed, the observed kurtosis per parcel and test date were comprised between 0.64 and 27.40. Skewness values ranged from -4.39 to -1.38. These high kurtosis values highlight that CSH records were not normally distributed per parcel. Therefore, the current way to use an average CSH to represent a parcel is not the best choice as this value is not representative. This implies the need to adopt an unbiased approach that enables the comparison of CSH and other variables between dates. The chosen method consists in splitting the parcels in square sub-blocks. Each cell of this grid gathers all the climatic-soil related-satellite-median CSH data and is used as the unitary entity to train the predictive model of the biomass available in the pasture. [less ▲]

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See detailIntegrated Water Resources Management: past, present and future
Wellens, Joost ULiege; Derouane, Johan; Pale, Sié ULiege et al

in Geo-Eco-Trop (2019)

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See detailDiagnostic d’un système d’informations de gestion de l’eau à usage agricole dans le sous-bassin versant de la Haute-Comoé, Burkina Faso.
Pale, Sié ULiege; Traoré, Farid; Wellens, Joost ULiege et al

in GEO-ECO-TROP (2019), 43(3, n.s.)

L’agriculture irriguée, moteur économique du sous-bassin versant de la Haute-Comoé, est confrontée à de nombreux défis, à cause entre autres de la mauvaise gestion des ressources en eau de surface, qui se ... [more ▼]

L’agriculture irriguée, moteur économique du sous-bassin versant de la Haute-Comoé, est confrontée à de nombreux défis, à cause entre autres de la mauvaise gestion des ressources en eau de surface, qui se raréfient. L’insuffisance de communications entre les usagers a conduit parfois à des tensions perceptibles. Au regard de ce constat et face à un système national inopérant, les parties prenantes de la « Gestion Intégrée des Ressources en Eau », à travers le Comité Local de l’Eau (CLE), ont décidé de mettre en place un système de production et de partage d’informations. Une enquête qualitative réalisée pendant les campagnes sèches 2016 et 2017, a permis d’analyser le système, de repérer ses forces et faiblesses et de faire des propositions. Il ressort de cette analyse que le programme de lâchers d’eau à partir des trois principaux réservoirs contrôlant les eaux de surface, est au centre du système d’information du CLE et sa principale force réside dans la démarche de production de l’information. Le maillon « diffusion », reste cependant faible et il devrait être amélioré par des outils de communication de masse. [less ▲]

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See detailThe performance of random forest classification based on phenological metrics derived from Sentinel-2 and landsat 8 to map crop cover in an irrigated semi-arid region
Htitiou, Abdelaziz; Boudhar, Abdelghani; Lebrini, Youssef et al

in Remote Sensing in Earth Systems Sciences (2019)

The use of remote sensing data provides valuable information to ensure sustainable land cover management. In this paper, the potential of phenological metrics data, derived from Sentinel-2A (S2) and ... [more ▼]

The use of remote sensing data provides valuable information to ensure sustainable land cover management. In this paper, the potential of phenological metrics data, derived from Sentinel-2A (S2) and Landsat 8 (L8) NDVI time series, was evaluated using Random Forest (RF) classification to identify and map various crop classes over two irrigated perimeters in Morocco. The smoothed NDVI time series obtained by the TIMESAT software was used to extract profiles and phenological metrics, which constitute potential explanatory variables for cropland classification. The method of classification applied involves the use of a supervised Random Forest (RF) classifier. The results demonstrated the capability of moderate-to-high spatial resolution (10–30 m) satellite imagery to capture the phenological stages of different cropping systems over the study area. Furthermore, the classification based on S2 data presents a higher overall accuracy of 93% and a kappa coefficient of 0.91 than those produced by L8 data, which are 90% and 0.88, respectively. In other words, phenological metrics obtained from S2 time series data showed high potential for agricultural crop-types classification in semi-arid regions and thus can constitute a valuable tool for decision makers to use in managing and monitoring a complex landscape such as an irrigated perimeter. [less ▲]

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See detailUse of precision farming practices and crop modelling for enhancing water and phosphorus efficiency
El-Mejjaouy, Yousra ULiege; Mamassi, Achraf ULiege; Chtouki, Mohamed ULiege et al

Poster (2019, October 07)

In a context of climate change, African agriculture aims at developing new approaches to face multiple constraints related to water scarcity, soil degradation or nutrients depletion. Nonrenewable ... [more ▼]

In a context of climate change, African agriculture aims at developing new approaches to face multiple constraints related to water scarcity, soil degradation or nutrients depletion. Nonrenewable resources such as phosphorus are of concern. Precision farming, as a new alternative to conventional agriculture, aims to improve crop productivity through the optimization of water and nutrients use efficiency. It considers the spatiotemporal variability of fields related to soil heterogeneity, plant nutrient needs and meteorological conditions through the growing season. For an effective management of soil and crop system, several new technologies have emerged, including soil-plant sensing, innovative crop management practices, and crop growth simulation and yield forecasting models. Regarding phosphorus management, use efficiency can be improved through the accurate assessment of phosphorus status in soil and plant. Proximal sensing based on visible near-infrared spectroscopy seems to be a promising alternative to manage soil fertility, understand phosphorus dynamics and enhance crop productivity. These aims can be also achieved by adopting hyper-frequent drip fertigation as an efficient agricultural practice, combined to hydrogeophysics to monitor water and nutrient fluxes in the soil-plant continuum. In addition, based on the interactions between meteorological conditions, soil properties and crop management, the use of agrometeorological models in simulation of crop growth parameters and forecasting crop production levels may allow assessing soil fertility and potential, ensuring an optimal future exploitation of farmland through the improvement of fertilization practices in an integrated management cropping system. [less ▲]

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See detailOpen Data and AquaCrop : sorghum yield estimates in support to food security in Niger
Mohamed Sallah, Abdoul-Hamid ULiege; Wellens, Joost ULiege; Garba, Issa et al

Poster (2019, September 20)

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See detailUtilisation du modèle dynamique de végétation CARAIB pour simuler les rendements en Belgique : validation et projections à l'horizon 2035
Jacquemin, Ingrid ULiege; Berckmans, Julie; Henrot, Alexandra-Jane ULiege et al

Poster (2019, September)

Le modèle CARAIB (CARbon Assimilation In the Biosphere) est un modèle dynamique de végétation initialement développé pour étudier le comportement de la végétation naturelle, tant son rôle dans le cycle ... [more ▼]

Le modèle CARAIB (CARbon Assimilation In the Biosphere) est un modèle dynamique de végétation initialement développé pour étudier le comportement de la végétation naturelle, tant son rôle dans le cycle global du carbone que sa réponse aux changements de climat et de sol. Afin de pouvoir répondre à de nouveaux challenges (comme l’étude des rétroactions climat-végétation ou encore de l’évaluation des services écosystémiques), le modèle a été doté d’un nouveau module lui permettant de couvrir l’ensemble de la végétation, naturelle et celle dite « managée » comme les cultures. Par conséquent, CARAIB devient un outil intéressant pour l’analyse du risque encouru par la végétation, et tout particulièrement pour les cultures agricoles, dans un contexte de changement climatique. Mais avant toute chose, il convient de procéder à la validation du module culture. Afin d’évaluer la variation temporelle, nous avons confronté les sorties du modèle avec des données de terrain venant des sites de mesure des flux d’eddy-covariance du réseau Fluxnet. Nous avons notamment comparé les flux de carbone (la GPP pour « Gross Primary Production » et la NEE pour « Net Ecosystem Exchange ») et l’évapotranspiration simulés par le modèle, avec les observations venant de plusieurs sites, dont celui de Lonzée en Belgique et de Grignon en France. A eux seuls, ces deux sites permettent de couvrir les 6 cultures proposées par CARAIB, à savoir le froment et l’orge d’hiver, le maïs, les pommes de terre, les betteraves sucrières et le colza. Pour l’évaluation de la variabilité spatiale, nous avons procédé à des simulations sur l’ensemble de la Belgique, où le modèle a été forcé par les sorties du modèle régional ALARO de l’Institut Royal Météorologique pour le passé récent, à 4km de résolution. Finalement, nous avons forcé le modèle CARAIB, toujours avec les sorties du modèle ALARO à 4km, mais cette fois pour les scénarios futurs RCP4.5 et 8.5, pour l’horizon 2035. Au-delà de l’effet fertilisant du CO2 atmosphérique croissant qui impacte positivement les rendements, nous pouvons d’ores et déjà mettre en évidence une variabilité interannuelle plus importante pour l’ensemble des cultures à l’exception du maïs. [less ▲]

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See detailEmploying weather-based disease model and machine learning techniques for optimal control of wheat stripe rust in Morocco
El Jarroudi, Moussa ULiege; Lahlali, Rachid; El Jarroudi, Haifa et al

in AI2SD2019 (Ed.) Advanced Intelligent Systems for Sustainable Development (2019, July 08)

Wheat stripe rust (WSR, caused by Puccinia striiformis Westend) is among the most important crop diseases causing a continuous threat to wheat production worldwide. In most seasons in temperate countries ... [more ▼]

Wheat stripe rust (WSR, caused by Puccinia striiformis Westend) is among the most important crop diseases causing a continuous threat to wheat production worldwide. In most seasons in temperate countries, environmental conditions during spring and early summer are conducive to the production of large quantities of spores of P. striiformis, which are dispersed from distances of a few centimeters to thousands of kilometers, where they might reach a susceptible host plant. Weather-based systems, or weather-based systems combined with other disease or agronomic variables have been implemented in decision-support systems (DSS) to determine whether fungicide sprays should be applied to prevent the risk of epidemics that might otherwise lead to yield loss. Given WSR is becoming a major threat in wheat-producing regions in Morocco, a DSS integrating a disease risk model would help limiting potentially harmful side effects of fungicide applications while ensuring economic benefits. The main objective of this study is to develop a threshold-based weather model for predicting in-season WSR progress in selected wheat-producing regions (i.e., Sais, Gharb, Middle Atlas, Tadla, Zair, Zemmour, Pre-Rif, High Atlas and Oasis) in Morocco. The threshold-based weather modelling approach has been successfully applied for predicting WSR in Belgium and Luxembourg. Data collected during two consecutive crop seasons in 2010-2011 at the selected sites will be used to test the modelling approach in Morocco. Machine learning techniques including Random Forest, Multivariate Adaptive Regression Splines, and Naïve Bayes Algorithm will also be investigated to improve the model. The reproducibility of area-specific modelling approaches is often a hurdle for their application in operational disease warning system at a regional scale. As such, this study is a validation case study of the threshold-based weather modelling approach. Moreover, it explores the potential utility of coupling artificial intelligence algorithms with plant disease models in decision support systems as an effort to improve sustainable wheat production in Morocco. [less ▲]

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See detailUsing multi-temporal landsat images and support vector machine to assess the changes in agricultural irrigated areas in the mogtedo region, burkina faso
Traoré, Farid; Bonkoungou, Joachim; Compaoré, Jérôme et al

in Remote Sensing (2019), 11(12),

Over the last few decades, small-scale irrigation has been implemented in Burkina Faso as a strategy to mitigate the impacts of adverse climate conditions. However, the development of irrigated perimeters ... [more ▼]

Over the last few decades, small-scale irrigation has been implemented in Burkina Faso as a strategy to mitigate the impacts of adverse climate conditions. However, the development of irrigated perimeters around small and medium water reservoirs has put the water resources under significant pressure, given the uncontrolled exploitation and lack of efficacious management plan. Insights into changes in irrigated areas around these reservoirs are therefore crucial for their sustainable management while meeting the different agricultural water needs. They will help to center policy priorities in terms of major impacts on the reservoirs; and thereby elaborate relevant mitigation and/or adaptation strategies. The main objectives of this study were to (1) quantify the changes in irrigated land areas surrounding the Mogtedo water reservoir between 1987 and 2015; and (2) determine whether the irrigable potential of this reservoir could sustainably meet the agricultural water needs under a more variable and changing climate. A low-cost remote sensing method based on Landsat imagery (Thematic Mapper, Enhanced Thematic Mapper Plus, and Operational Land Imager) and using Support Vector Machine (SVM) classification was developed to detect the changes in proportion of land use/land cover (LULC) in the Mogtedo region. A forward and backward change detection analysis requiring agronomic expertise was also applied to correct the pixels temporal trajectories. In addition, an intensity analysis was performed to assess land changes at time intervals, category, and transition levels. Five main LULC classes were identified: bare and hydromorphic soils, irrigated and rainfed agricultural areas, and water bodies. Overall, the classification of LULC was satisfactory with the overall accuracy and kappa coefficients ranging from 94.22 to 95.60% and 0.92 to 0.94, respectively. Results showed that LULC transformations were faster between 2000 and 2015, compared to the 1987-2000 period. The majority of categories (LULC classes) were active in terms of intensity of change (gain or loss) during the 1987-2000 and 2000-2015 periods, except hydromorphic soils. During these periods, the transition from rainfed agricultural areas to irrigated agricultural areas were targeted and stationary. Our findings revealed a 54% increase in irrigated areas between 1987 and 2015. The reservoir water volume decreased markedly from 9,077,000 m3 to 7,100,000 m3 during the same period. Such a decrease threatens the satisfaction of agricultural water requirements, since the reservoir is the unique source of irrigation water in the region. It could potentially lead to conflicts between users if adequate strategies for the sustainable management of the Mogtedo reservoir are not implemented. The methodology used in this study also addressed the challenge of building up historical spatial information database in data-scarce environments, and could be replicated readily in regions or countries like Burkina Faso. © 2019 by the authors. [less ▲]

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See detailAssessment of Livelihood Vulnerability to Drought: A Case Study in Dak Nong Province, Vietnam
Thao, N. T. T.; Khoi, D. N.; Xuan, T. T. et al

in International Journal of Disaster Risk Science (2019)

In recent years, droughts have strongly affected the Central Highlands of Vietnam and have resulted in crop damage, yield decline, and serious water shortage. This study investigated the livelihood ... [more ▼]

In recent years, droughts have strongly affected the Central Highlands of Vietnam and have resulted in crop damage, yield decline, and serious water shortage. This study investigated the livelihood vulnerability of five communities of farmers who are exposed to droughts in one of the more vulnerable regions of Vietnam—Dak Nong Province. A survey of 250 households was conducted in the five communities to collect data on the region’s sociodemographic profile, livelihood systems, social networks, health status, food and water security, drought conditions, and climate variability. Data were aggregated using a livelihood vulnerability index and the IPCC vulnerability index. The survey results indicate that Quang Phu community is the most vulnerable of the study’s communities, followed by Nam N’dir, Dak Nang, Duc Xuyen, and Dak D’ro in descending order of vulnerability. Water availability and livelihood strategies are the most important variables in determining the vulnerability of the five surveyed communities. In order to reduce vulnerability to droughts, water management practices and livelihood diversification in farming and nonfarming activities are recommended for the study area. © 2019, The Author(s). [less ▲]

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See detailBatch-processing of AquaCrop plug-in for rainfed maize using satellite derived Fractional Vegetation Cover data
Mohamed Sallah, Abdoul-Hamid ULiege; Tychon, Bernard ULiege; Piccard, Isabelle et al

in Agricultural Water Management (2019), 217(346-355),

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See detailLE PEUPLEMENT BIOLOGIQUE DANS LES ÉCOSYSTÈMES AQUATIQUES FACE AUX POLLUANTS ÉMERGENTS : MENACES ET RISQUES
Sossey Alaoui, Khadija ULiege; Tychon, Bernard ULiege

Scientific conference (2018, October 22)

Le projet DIADeM a organisé le 22 octobre 2018 un séminaire au Campus d'Arlon de l'Université de Liège en Belgique. Le Séminaire est organisé par les acteurs scientifiques de l’Uliège, Campus d’Arlon en ... [more ▼]

Le projet DIADeM a organisé le 22 octobre 2018 un séminaire au Campus d'Arlon de l'Université de Liège en Belgique. Le Séminaire est organisé par les acteurs scientifiques de l’Uliège, Campus d’Arlon en collaboration avec les partenaires du projet DIADEM afin de mettre l’accent sur la menace et le danger que les écosystèmes et la biodiversité encourent face aux polluants émergents. Il vise également à mettre en évidence les outils développés pour améliorer le diagnostic des écosystèmes aquatiques. Les déchets ménagers, les hôpitaux, les animaux d’élevage sont des sources importantes de rejets médicamenteux et peuvent polluer les sols et les eaux. Partiellement éliminés par les stations d’épurations, ces résidus pharmaceutiques se retrouvent sur les sols, dans les eaux superficielles et dans les eaux souterraines. Une fois dans l'environnement, elles peuvent contaminer les organismes vivants et potentiellement les affecter. [less ▲]

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See detailModeling the Main Fungal Diseases of Winter Wheat: Constraints and Possible Solutions
El Jarroudi, Moussa ULiege; Kouadio, Amani Louis ULiege; Tychon, Bernard ULiege et al

in Advances in Plant Pathology (2018)

The first step in the formulation of disease management strategy for any cropping system is to identify the most important risk factors. This is facilitated by basic epidemiological studies of pathogen ... [more ▼]

The first step in the formulation of disease management strategy for any cropping system is to identify the most important risk factors. This is facilitated by basic epidemiological studies of pathogen life cycles, and an understanding of the way in which weather and cropping factors affect the quantity of initial inoculum and the rate at which the epidemic develops. Weather conditions are important factors in the development of fungal diseases in winter wheat, and constitute the main inputs of the decision support systems used to forecast disease and thus determine the timing for efficacious fungicide application. Crop protection often relies on preventive fungicide applications. Considering the slim cost−revenue ratio for winter wheat and the negative environmental impacts of fungicide overuse, necessity for applying only sprays that are critical for disease control becomes paramount for a sustainable and environmentally friendly crop production. Thus, fungicides should only be applied at critical stages for disease development, and only after the pathogen has been correctly identified. This chapter provides an overview of different weather-based disease models developed for assessing the real-time risk of epidemic development of the major fungal diseases (Septoria leaf blotch, leaf rusts and Fusarium head blight) of winter wheat in Luxembourg. [less ▲]

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