Predicting Wheat Yield Gap and its Determinants in Rainfed Mediterranean Climate of Morocco: Using Ground Information, Satellite Images and Machine Learning

Wheat is the main food crop grown in more than 2.8 million ha in Morocco and almost 16.8 million ha in 21 Middle East and North Africa (MENA) region countries. It is primarily grown in rainfed conditions in the country and in MENA region, with diverse soil and climatic conditions and a varying range of rainfall patterns, mainly characterized by drought due to poor rainfall distribution within the season. Large disparities in attainable yield and profit gaps have been reported, and closing these gaps is important for meeting domestic demand and reducing imports. The main aim of this study was to determine field- and landscape-level yield and yield gaps for wheat and its drivers in the Central region of Morocco using ground information, remote sensing and machine learning approaches. To this end, we prepared a time series map of six vegetation indices (EVI2, CGVI, MSR, NDVI, OSAVI, and RVI) derived from Sentinel-2 images (10 m) over three consecutive crop seasons (2018-2019, 2019-2020, and 2020-2021). Vegetation indices datasets were combined with the climate, soil, and crop management, and the random forest model was calibrated and validated for each cropping season. The models that gave good performance were applied to predict actual yield, potential yield, and the yield gaps at the plot level. The models were used for mapping yield at the regional scale, Rabat-Sale-Kenitra region of Morocco. Based on those datasets, the main drivers of this gap were determined. The findings reveal that RVI, EVI2, and GCVI vegetation indices well predicted wheat yield for the 2018-2019, 2019-2020, and 2020-2021 seasons with R2 of 0.869, 0.863, and 0.844, respectively. The predicted rainfed potential wheat yields were 5.99, 1.53, and 4.66 t per ha, respectively for three crop seasons. Combined over all three seasons, the most important yield determinants are soil moisture, cumulative rainfall during the crop growing period, followed by actual evapotranspiration, and silt content of the soil. When combining soil, climate and management practices in 2019-2020, the major determinants are still soil moisture and the variables of climate followed by the management practices and soil texture. The results and maps produced are of great importance for predicting wheat yield in advance using in-season vegetation indices which is important for the farmers and policymakers for planning at regional and national scales

Geospatial Assessment of Soil Organic Matter Variability at Sidi Bennour District in Doukkala Plain in Morocco

Understanding the spatial variability of soil organic matter (SOM) is critical for studying and assessing soil fertility and quality. This knowledge is important for soil management, which results in high crop yields at a reduced cost of crop production and helps to protect the environment. The benefits of an accurate interpolation of SOM spatial distribution are well known at the agricultural, economic, and ecological levels. It has been conducted this study for comparing and analyze different spatial interpolation methods to evaluate the spatial distribution of SOM in Sidi Bennour District, which is a semi-arid area in the irrigated scheme of the Doukkala Plain, Morocco. For conding this study, it was collected 368 representative soil samples at a depth of 0–30 cm. A portable global positioning system was used to obtain the location coordinates of soil sampling sites. The SOM spatial distribution was performed using four interpolation methods: inverse distance weighted and local polynomial interpolation as deterministic methods, and ordinary kriging and empirical Bayesian kriging as geostatistical methods. High SOM levels were concentrated in vertisols, and low SOM levels were observed in immature soils. The average SOM value was 1.346%, with moderate to high variability (CV = 35.720%). A low SOM concentration indicates a continuous possibility of soil degradation in the future. Ordinary kriging yielded better results than the other interpolation methods (RMSE = 0.395) with a semivariogram fitted by an exponential model, followed by inverse distance weighted (RMSE = 0.397), empirical Bayesian kriging (RMSE = 0.400), and local polynomial interpolation (RMSE = 0.412). Soil genetics and the strong influence of human activity are the major sources of SOM spatial dependence, which is moderate to low. Low SOM content levels (< 1.15%) were present in the southwestern and eastern parts of the digital map. This situation calls for the implementation of specific soil recovery measures

Assessing the impact of sampling strategy in random forest-based predicting of soil nutrients: a study case from northern Morocco

In this work, we tested different combinations of sampling strategies, random sampling and conditioned Latin Hypercube sampling (cLHS)] and sample ratios (10% = 147 and 25% = 368) to predict soil phosphorus and potassium contents, previously estimated using standard laboratory protocols. Other environmental covariates, used as input data for prediction, were obtained from different sources (multispectral Landsat-OLI 8 image, WorldClim database, ISRIC soil database, and ASTER-GDEM). Our findings showed that random sampling was suitable for predicting phosphorus, while the conditioned Latin Hypercube sampling was suitable for predicting potassium. Furthermore, we observed that when the sample ratio increased from 10 to 25%, model accuracy improved in random sampling and cLHS for phosphorus and potassium prediction. However, before generalizing these findings, we recommend that further studies be conducted under different conditions (climate, soil types and parent materials) and testing other sample ratios to determine the best sampling strategy with the optimum ratio to predict soil nutrients better