Assessing the CO2 fertilization effect on cereal yield in Morocco using the CARAIB dynamic vegetation model driven by Med-CORDEX projections

In Morocco, the economic weight of agriculture is so high that any temporal trend or seasonality change in the climate will immediately affect the country economy, particularly that involving crops used as the basis of food security like cereals. It is therefore necessary to develop knowledge about CO2 fertilization effect on cereal crops and strengthen forecasting systems for predicting the impacts of climate change.</p><p>Dynamic Vegetation Models can be used to investigate and interpret vegetation trends related to increasing levels of atmospheric CO2. In fact, an increase in CO2 concentration causes an elevated photosynthesis rate, resulting in more energy and thus a quicker development of the plant. On the other hand, it reduces the amount of water needed to produce an equivalent amount of biomass. Hence in dry areas like Morocco, it may significantly alter future crop production and reduce the negative effects of climate change on agricultural yields.</p><p>CARAIB (CARbon Assimilation In the Biosphere) is a dynamic vegetation model developed to study the role of vegetation in the global carbon cycle and to study vegetation distribution in the past, the present, and in the future. The model is composed of several modules dealing with soil hydrology, photosynthesis and stomatal regulation, carbon allocation and biomass growth, soil and litter carbon dynamics, and natural vegetation fires. CARAIB was improved by the addition of the crop module. In fact, crop growth is driven by photosynthetic activity but differs on the use of phenological stages. Two stages are defined (from sowing to emergence, and from emergence to harvesting). These stages are completed when a prescribed level of heat is reached based on the growing degree days. The yield is then estimated from net primary productivity using a harvest index.</p><p>The simulations are performed across all Morocco. The three main cereal crops simulated include soft wheat, durum wheat, and barley, they are grown in all agro-ecological zones. The simulation of the recent period was dedicated to the validation of the crop module over Morocco. For temporal and spatial validation, we used yearly yield data collected between 1997 and 2017 at the scale of the smallest territorial unit which is the municipality. To assess the impact of CO2 concentration on cereal yield, we are using 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 driven by two Representative Concentration Pathway scenarios (RCP4.5 and RCP8.5) and three horizons (2020-2040, 2041–2060 and 2081–2100). Modeling is conducted twice, one with an annually adapted concentration according to the RCPs, and another one with fixed concentration to separate the influence of CO2 from that of the other input variables.</p&gt

Cartographie du risque d'incendies de forêt dans la région de Chefchaouen-Ouazzane (Maroc)

This study explores the possibility to predict the spatial distribution of forest fire ignitions in Chefchaouen-Ouazzane region (North-west of Morocco). The geographic information system was used to locate 613 forest fires recorded between 2002 and 2015. The building of dichotomous prediction model was developed based on the results of the binary logistic regression using 20 human and biophysical variables. A random sample of 2141 observations (60%) was used to build the model and 1427 external observations (40%) have been reserved for testing the ability of the model to predict forest fire ignitions. The explanatory variables included in the model, report on the impact of factors related to (1) human action represented by accessibility (roads and trails) and locations of great fires recurrences, (2) topo-climatic, including, the maximum daily temperature, relative air humidity and the slopes and (3) biological, namely the type of fuel, including firstly cork oak with a low density, and secondly the Matorral, the Lentiscus, the Erica, the Cistus and Kermes. The binary logistic model correctly classifies 88.1% of the sample reserved for the model building (2141 cases) and 86.9 % of validation data (validation test with 1427 observations). The map produced could operationally improve the alerts processes, the lookout posts positioning and the early intervention against fires by the units in charge of initial attacks. It should be emphasized that in this study ways of improvement are proposed in order to increase the accuracy of the forest fire ignitions probability map. Keywords: GIS, Spatial modeling, Forest fire ignition, Predictive mapping, Logistic regression, Chefchaouen-OuazzaneCette étude examine la possibilité de prédire la répartition spatiale des éclosions des feux de forêts dans la région de Chefchaouen-Ouazzane (Nord-ouest du Maroc). Le système d’information géographique a été utilisé pour la localisation de 613 feux de forêt, enregistrés entre 2002 et 2015. La construction du modèle de prédiction dichotomique a été développée sur la base des résultats de la régression logistique binaire en utilisant 20 variables explicatives anthropiques et biophysiques. Un échantillon aléatoire de 2141 observations (60%) a été utilisé pour la construction du modèle et 1427 observations exogènes (40 %) ont été réservées pour la réalisation d’un test indépendant de la capacité du modèle à prédire les éclosions des feux. Les variables explicatives incluses dans le modèle, font état de l’impact des facteurs (1) anthropiques, représentés par les voies d’accès (pistes et routes) et les lieux de grande récurrences d’incendies, (2) topo-climatiques, dont la température maximale journalière, l’humidité relative de l’air et les pentes, et enfin (3) biologiques, à savoir le type de combustible à base notamment de chêne-liège de faible densité, d’une part, et du Matorral, du Lentisque, de l’Erica, du Cistus et du Kermès, d’autre part. Le modèle logistique binaire classe correctement 88,1% de l’échantillon d’étalonnage du modèle (2141 observations) et de 86,9% des cas de validation (test de validation avec 1427 observations). La carte produite pourrait, sur le plan opérationnel, améliorer les processus d’alertes et le positionnement des postes de guets et des unités d’interventions chargées des attaques initiales des feux naissants. Des voies d’amélioration sont également proposées pour augmenter sa précision. Mots clés: SIG, Modélisation spatiale, Éclosion des feux de forêt, Carte de prévision, Régression logistiqu

Cereal yield forecasting in Morocco using the CARAIB dynamic vegetation model driven by HadGEM2-AO projections

Food security, in Morocco as in many parts of the world, depends heavily on cereal production which fluctuates relying on weather conditions. In fact, Morocco has a production system for cereals which is dominated by rainfed. It is therefore necessary to further develop knowledge about climate change and strengthen forecasting systems for predicting the impacts of climate change. Our research, funded by a bilateral project of Wallonie-Bruxelles International, aims to study the response of cereal production 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. For crops, a specific module describes basic management parameters (sowing, harvest, rotation) and phenological phases. The simulations are performed across all Morocco using different input data. The three main cereal crops simulated include soft wheat, durum wheat and barley, they are grown in all provinces and all agro-ecological zones. Regarding climatic inputs, we’re using two sets of data: the first one is interpolated and bias-corrected fields from the climate model HadGEM2-AO for the historical period (1990-2005), in addition to three different Representative Concentration Pathway scenarios (RCP2.6, RCP4.5 and RCP8.5) from 2005 to 2100. The second one is high resolution (30 arc sec) gridded climate data derived from WorldClim combined with interpolated anomalies from CRU (Climatic Research Unit) over the historical period 1990 to 2018. After obtaining preliminary results for the past period, and in order to improve the prediction using the field data which are the observed yields, we performed a sensitivity analysis. We used the One-at-a-time (OAT) approach by moving one input variable, keeping others at their baseline (nominal) values, then, returning the variable to its nominal value, then repeating for each of the other inputs in the same way. Sensitivity may then be measured by monitoring changes in the output, using linear regression. The inputs studied are the initial value of carbon pool, leaf C/N ratio, water stress, sowing date, GDD harvest, stomatal conductance parameters, specific leaf area, and rooting depth

A comparison of empirical and mechanistic models for wheat yield prediction at field level in Moroccan rainfed areas

peer reviewedIn the context of climate change, in-season and longer-term yield predictions are needed to anticipate local and regional food crises and propose adaptations to farmers’ practices. Mechanistic models and machine learning are two modelling options to consider in this perspective. In this study, regression (MR) and Random Forest (RF) models were calibrated for wheat yield prediction in Morocco, using data collected from 125 farmers’ wheat fields. Additionally , MR and RF models were calibrated both with or without remotely-sensed leaf area index (LAI), while considering all farmers’ fields, or specifically to agroecological zoning in Morocco. The same farmers’ fields were simulated using a mechanistic model (APSIM-wheat). We compared the predictive performances of the empirical models and APSIM-wheat. Results showed that both MR and RF showed rather good predictive quality (NRMSEs below 35%), but were always outperformed by APSIM model. Both RF and MR selected remotely-sensed LAI at heading, climate variables (maximal temperatures at emergence and tillering), and fertilization practices (amount of nitrogen applied at heading) as major yield predictors. Integration of remotely-sensed LAI in the calibration process reduced NRMSE of 4.5% and 1.8 % on average for MR and RF models respectively. Calibration of region specific models did not significantly improve the predictive. These findings lead to the conclusion that mechanistic models are better at capturing the impacts of in-season climate variability and would be preferred to support short term tactical adjustments to farmers’ practices, while machine learning models are easier to use in the perspective of mid-term regional prediction.SoilPhorLife-Projet

Simulating and analysing climate change impacts on crop yields in Morocco using the CARAIB dynamic vegetation model driven by Med- CORDEX projections

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)

An assessment of empirical models, structure, predictor variables, and performances for wheat yield prediction at field level in Moroccan rainfed areas.

peer reviewedRelationship between the performances of yield prediction models, complexity of their structures and the number and type of required input was always a recognized problematic by researchers. In the present study, we compared extracted empirical models with a previous calibrated and evaluated mechanistic model (APSIM-wheat) in yield prediction at field scale, by highlighting empirical models structure, predictor variables, their sustainability and the timely scope of yield prediction, and assessing the impact of integrating satellite-based dataset on models accuracy. We conducted a modelling framework for wheat yield prediction in Moroccan rainfed areas basing on two methods: multiple regression (MR) and random forest (RF) algorithms, and using input parameters database combine soil, climate, remotely-sensed LAI and crop management variables that were collected over three successive crop seasons (2018-2021) from 130 farmers¿ wheat fields located in Moroccan rainfed areas. Results show the relevance of remotely-sensed LAI-Z50, nitrogen fertilization and climate variables as predictors of yield. Almost identical wheat yield estimation performances using both empirical methods with RMSE < 0.9 t.ha-1 in most cases, whereas, APSIM-wheat has the highest potential in predicting wheat yield at field scale. Also, clear progresses were observed in models robustness when integrating LAI satellite-based parameters during empirical models development