Harmonized in situ datasets for agricultural land use mapping and monitoring in tropical countries

The availability of crop type reference datasets for satellite image classification is very limited for complex agricultural systems as observed in developing and emerging countries. Indeed, agricultural land use is very dynamic, agricultural censuses are often poorly georeferenced and crop types are difficult to interpret directly from satellite imagery. In this paper, we present a database made of 24 datasets collected in a standardized manner over nine sites within the framework of the international JECAM (Joint Experiment for Crop Assessment and Monitoring) initiative; the sites were spread over seven countries of the tropical belt, and the number of data collection years depended on the site (from 1 to 7 years between 2013 and 2020). These quality-controlled datasets are distinguished by in situ data collected at the field scale by local experts, with precise geographic coordinates, and following a common protocol. Altogether, the datasets completed 27 074 polygons (20 257 crops and 6817 noncrops, ranging from 748 plots in 2013 (one site visited) to 5515 in 2015 (six sites visited)) documented by detailed keywords. These datasets can be used to produce and validate agricultural land use maps in the tropics. They can also be used to assess the performances and robustness of classification methods of cropland and crop types/practices in a large range of tropical farming systems. The dataset is available at https://doi.org/10.18167/DVN1/P7OLAP (Jolivot et al., 2021).

Phosphogypsum Organic, a Byproduct from Rare-Earth Metals Processing, Improves Plant and Soil

Phosphogypsum organic (PG organic) is a soil conditioner, derived from residues, water leach purification (WLP) and neutralisation underflow (NUF) from rare-earth metals processing in combination with composted organic material. There was no report available with regards to the effectiveness of this byproduct for crops improvement in a sandy soil texture. Therefore, a field trial involving a multi-crop was conducted by the addition of PG organic on a sandy texture soil for 23-month period. Guinea grass or guinea grass intercropping with teak wood trees, corn and kenaf showed an improvement in cumulative fresh yield in plot treated with PG organic either with a half- or full-fertilizer recommended rate for the respective crop as compared to control. The same trend was also observed in teak wood trees in hole planting systems and pandan coconut seedlings in the polybags. Application of PG organic in each season showed a consistently higher cumulative fresh yield or yield for certain crop types due to soil ability to maintain the soil pH buffering capacity (pH 5.8–6.0). Therefore, the application of PG organic as soil conditioner promotes plant growth and development due to the improvement of soil condition by creating suitable ecosystem for nutrients absorption by roots.

Crop Field Classification using Data Fusion of Unmanned Aerial Vehicle (UAV) and Sentinel 2A satellite: the case of Oda Dhawata Kebele Cluster farmland, Oromia Region, Ethiopia

Accurate crop classification using remote sensing based satellite imageries approach remains challenging due to mix in spectral signatures. Employing Unmanned Aerial Vehicle (UAV) together with satellite imageries is believed in improving crop classification at field. Accordingly, this study aims to evaluate the potential of UAV images by blending with Sentinel 2A satellite images for crop field classification in Ethiopian agricultural context. The main purpose of the blending is to upgrade and or improve the lower resolution of the data source that is the sentinel 2A data which was 10m resolution. In the study, UAV data was used and preprocessed. The preprocessing includes camera calibration, photo alignment, dense point cloud generation based on the estimated camera positioning of scouting crop types. Then, orthomosaic UAV image was generated from single dense point cloud. Then, the processed UAV data was fused with Sentinel 2A (medium resolution) satellite data using Gram Schmidt pan sharpening method.this method is the most approach that it can run large data sets of spatial resultions. For crop classification, the Random forest (RF) machine-learning algorithm and Maximum likelihood methods were applied. Apart from the UAV and S2A data, field data was collected for training the crop classification. The point field data was collected from Teff, Wheat, Faba bean, Barley and Sorghum crop fields The results show that RF classifier algorithm classifies the crop types with 94% overall accuracy whereas the Maximum likelihood classifier with 90% overall accuracy. This implies that fused image has a potential to be used for crop type classification together with relatively better classification technique with high accuracy level

Synergy of Sentinel-1 and Sentinel-2 Imagery for Early Seasonal Agricultural Crop Mapping

The exploitation of the unprecedented capacity of Sentinel-1 (S1) and Sentinel-2 (S2) data offers new opportunities for crop mapping. In the framework of the SenSAgri project, this work studies the synergy of very high-resolution Sentinel time series to produce accurate early seasonal binary cropland mask and crop type map products. A crop classification processing chain is proposed to address the following: (1) high dimensionality challenges arising from the explosive growth in available satellite observations and (2) the scarcity of training data. The two-fold methodology is based on an S1-S2 classification system combining the so-called soft output predictions of two individually trained classifiers. The performances of the SenSAgri processing chain were assessed over three European test sites characterized by different agricultural systems. A large number of highly diverse and independent data sets were used for validation experiments. The agreement between independent classification algorithms of the Sentinel data was confirmed through different experiments. The presented results assess the interest of decision-level fusion strategies, such as the product of experts. Accurate crop map products were obtained over different countries in the early season with limited training data. The results highlight the benefit of fusion for early crop mapping and the interest of detecting cropland areas before the identification of crop types.

Regionalizing crop types to enhance global ecosystem modelling of maize production

Improving the prediction of crop production is critical for strategy development associated with global food security, particularly as the climate continues to change. Process-based ecosystem models are increasingly used for simulating global agricultural production. However, such simulations often use a single crop variety in global assessments, implying that major crops are identical across all regions of the world. To address this limitation, we applied a Bayesian approach to calibrate regional types of maize (Zea mays L.), capturing the aggregated traits of local varieties, for DayCent ecosystem model simulations, using global crop production data from 2001 to 2013. We selected major cropping regions from the FAO Global Agro-Environmental Stratification as a basis for the regionalization and identified the most important model parameters through a global sensitivity analysis. We calibrated DayCent using the sampling importance resampling algorithm and found significant improvement in DayCent simulations of maize yields with the calibrated regional varieties. Compared to a single type of maize for the world, the regionalization of maize leads to reductions in root mean squared error of 11%, 31%, 27%, 30%, 19%, and 27% and reductions in bias of 59%, 59%, 50%, 81%, 32%, and 56% for Africa, East Asia, Europe, North America, South America, and South & Southeast Asia, respectively. We also found the optimum parameter values of radiation use efficiency are positively correlated with the income level of different regions, which indicates that breeding has enhanced the photosynthetic efficiency of maize in developed countries. There may also be opportunities for expanding crop breeding programs in developing countries to enhance photosynthesis efficiency and reduce the yield gap in these regions. This study highlights the importance of representing regional variation in crop types for achieving accurate predictions of crop yields.

Quantifying the nutritional and income loss due to crop raiding in rural African subsistence farming community in South Africa

Globally, crop damage by wildlife contributes to food insecurity through direct loss of food and income. We investigated the calories lost and potential economic impact of crop raiding to subsistence homesteads abutting the Hluhluwe Game Reserve and assessed their mitigation measures to combat crop raiding. We quantified the seasonal loss of calories (KJ/g) of four common crops: beetroot (Beta vulgaris), common bean (Phaseolus vulgaris), maize (Zea mays) and spinach (Spinacia oleracea) and determined seasonal potential income loss based on local market cost of crops. Experimental data used for this study were collected from April 2016-March 2017 and questionnaire data were collected in March 2016, using a stratified sampling approach to sample the homesteads. We selected every second homestead for the interview and restricted the survey to one respondent per homestead to avoid pseudo-replication of results. In the one year of sampling, we did not record any large mammals crop raiding, both from direct observations and camera trap footage, we also did not find a statistically significant relationship between the level of damage and distance of farms from the reserve boundary. Throughout the study, we captured a total of 96 individual rodents comprising of two species: red bush rat (Aethomys spp.; 67.7%; 51 males and 28 females) and pouched mouse (Saccostomus campestris; 32.3% (14 females and three males ) and we used the damage caused by these animals and other small animals to quantify the level of damage. We found that season, crop type, farm slope and the interaction between season and crop type were significant predictors of relative calorie loss. Again, season, crop type and the interaction between season and crop type were significant predictors of the potential income loss, with the highest income loss recorded for spinach in the dry season. In addition, significant differences were found for potential income loss for all crop types in the wet season, and for the interaction between crop types maize, spinach and the wet season, but no significant difference was found for crop type common bean and the wet season. A multinomial regression analysis revealed that crop raiding animal type, crop types raided and distance of farms from the reserve all had a significant effect on the choice of mitigation measures farmers used. Most importantly we found the highest relative calorie loss for maize during the dry season, which could impact on subsistence farmers by reducing their daily calorie intake and impact on their food security especially during the season where subsistence farming is slow. Moreover, as the most preferred mitigation measure by farmers can have opportunity costs to this community, such as the loss of school time for children. These results have important implications for food security policies and socially related policies and practices.