Ethiopian Crop Type 2020 (EthCT2020) dataset: Crop type data for environmental and agricultural remote sensing applications in complex Ethiopian smallholder wheat-based farming systems (Meher season 2020/21).

Crop type observation is crucial for various environmental and agricultural remote sensing applications including land use and land cover mapping, crop growth monitoring, crop modelling, yield forecasting, disease surveillance, and climate modelling. Quality-controlled georeferenced crop type information is essential for calibrating and validating machine learning algorithms. However, publicly available field data is scarce, particularly in the highly dynamic smallholder farming systems of sub-Saharan Africa. For the 2020/21 main cropping season (Meher), the Ethiopian Crop Type 2020 (EthCT2020) dataset compiled from multiple sources provides 2,793 harmonized, quality-controlled, and georeferenced in-situ samples on annual crop types (7 crop groups; 22 crop classes) at smallholder field level across the complex and highly fragmented agricultural landscape of Ethiopia. The focus was on rainfed, wheat-based farming systems. A nationwide ground data collection campaign (GDCC; Source 1) was designed using a stratification approach based on wheat crop calendar information, and 1,263 in-situ data samples were collected in selected sampling regions. This in-situ data pool was enriched with 1,530 wheat samples extracted from a) the Wheat Rust Toolbox (WRTB; Source 2; 734 samples), a database for wheat disease surveillance data [1] and b) an inhouse farm household survey database (FHSD; Source 3; 796 samples). Obtained field data was labelled according to the Joint Experiment for Crop Assessment and Monitoring (JECAM) guidelines for cropland and crop type definition and field data collection [2] and the FAO Indicative Crop Classification [3]. The EthCT2020 dataset underwent extensive processing including data harmonization, mixed pixel assessment through visual interpretation using 5 m Planet satellite image composites, and quality-control using Sentinel-2 NDVI homogeneity analysis. The EthCT2020 dataset is unique in terms of crop diversity, pixel purity, and spatial accuracy while targeting a countrywide distribution. It is representative of Ethiopia's complex and highly fragmented agricultural landscape and can be useful for developing new machine learning algorithms for land use land cover mapping, crop type mapping, agricultural monitoring, and yield forecasting in smallholder cropping systems. The dataset can also serve as a baseline input parameter for crop models, climate models, and crop disease and pest forecasting systems.

The potential of UAV and very high-resolution satellite imagery for yellow and stem rust detection and phenotyping in Ethiopia.

Very high (spatial and temporal) resolution satellite (VHRS) and high-resolution unmanned aerial vehicle (UAV) imagery provides the opportunity to develop new crop disease detection methods at early growth stages with utility for early warning systems. The capability of multispectral UAV, SkySat and Pleiades imagery as a high throughput phenotyping (HTP) and rapid disease detection tool for wheat rusts is assessed. In a randomized trial with and without fungicide control, six bread wheat varieties with differing rust resistance were monitored using UAV and VHRS. In total, 18 spectral features served as predictors for stem and yellow rust disease progression and associated yield loss. Several spectral features demonstrated strong predictive power for the detection of combined wheat rust diseases and the estimation of varieties' response to disease stress and grain yield. Visible spectral (VIS) bands (Green, Red) were more useful at booting, shifting to VIS-NIR (near-infrared) vegetation indices (e.g., NDVI, RVI) at heading. The top-performing spectral features for disease progression and grain yield were the Red band and UAV-derived RVI and NDVI. Our findings provide valuable insight into the upscaling capability of multispectral sensors for disease detection, demonstrating the possibility of upscaling disease detection from plot to regional scales at early growth stages.

Pathways to wheat self-sufficiency in Africa.

A growing urban population and dietary changes increased wheat import bills in Africa to 9% per year. Though wheat production in the continent has been increasing over the past decades, to varying degrees depending on regions, this has not been commensurate with the rapidly increasing demand for wheat. Analyses of wheat yield gaps show that there is ample opportunity to increase wheat production in Africa through improved genetics and agronomic practices. Doing so would reduce import dependency and increase wheat self-sufficiency at national level in many African countries. In view of the uncertainties revealed by the global COVID-19 pandemic, extreme weather events, and world security issues, national policies in Africa should re-consider the value of self-sufficiency in production of staple food crops, specifically wheat. This is particularly so for areas where water-limited wheat yield gaps can be narrowed through intensification on existing cropland and judicious expansion of rainfed and irrigated wheat areas. Increasing the production of other sources of calories (and proteins) should also be considered to reduce dependency on wheat imports.

Distribution, dynamics, and physiological races of wheat stem rust (Puccinia graminis f.sp. tritici) on irrigated wheat in the Awash River Basin of Ethiopia.

Wheat is one of the high-value major crops in the world. However, wheat stem rust is considered one of the determinant threats to wheat production in Ethiopia and the world. So this study was conducted to assess the disease intensity, seasonal distribution dynamics pattern, the genetic variability of Puccinia graminis f. sp. tritici, and to determine the virulence spectrum in the irrigated ecology of the Awash River Basin. Totally 137 wheat farms were evaluated, from 2014/15-2019/20 in six districts representing the Upper, Middle, and Lower Awash River Basin. Farm plots were assessed, in every 5-10 km intervals, with 'X' fashion, and data on disease incidence, severity, healthy plants were counted and recorded. Diseased samples were collected from the diseased wheat stem by Puccinia graminis physiological and genetic race analysis. The seasonal trend of stem rust disease progress showed its importance to infer the future progresses of the disease for the country's potential production plan of irrigated wheat. The result revealed that the disease prevalence, disease incidence, and severity were significantly varied; among the different districts and seasons in the two regions. The survey results also indicated that about 71.7% of the wheat fields were affected by stem rust during the 2018/19 growing period. The disease's overall incidence and mean severity during the same season were 49.02% and 29.27%, respectively. In 2019/20, about 63.7% of the wheat fields were affected by stem rust, disease incidence 30.97%, and severity 17.22% were lower than the previous season. In 2019/20, even though seasonal disease distribution decreased, the spatial distribution was expanding in Afambo and Dubti districts. Four, stem rust dominant races were identified (TTTTF, TKTTF TKKTF, and TTKTF) by physiological and genetic race analysis during 2018/19 and one additional race (TKPTF) in 2019/20, production year. The result indicated that the races are highly virulent and affect most Sr genes except Sr31 and Sr24. From the race analysis result, TTTTF, and TKKTF have the broadest virulence spectrum race, which affects 90% of the Sr genes. Generally, we can conclude that the spatial and seasonal distribution of the disease is expanding. Most of the races in the irrigated areas in the Basin were similar to that of rain-fed wheat production belts in Ethiopia, so care must be given, to effective management of the diseases, in both production ecologies towards controlling the spore pressure than race variability. Therefore, these findings provide inputs for wheat producers to reduce the spread and disease' damage in the irrigated ecologies of Ethiopia. Also, it gives an insight for breeders to think about the breeding program in their crossing lines.

Multi-Temporal and Spectral Analysis of High-Resolution Hyperspectral Airborne Imagery for Precision Agriculture: Assessment of Wheat Grain Yield and Grain Protein Content.

This study evaluates the potential of high resolution hyperspectral airborne imagery to capture within-field variability of durum wheat grain yield (GY) and grain protein content (GPC) in two commercial fields in the Yaqui Valley (northwestern Mexico). Through a weekly/biweekly airborne flight campaign, we acquired 10 mosaics with a micro-hyperspectral Vis-NIR imaging sensor ranging from 400-850 nanometres (nm). Just before harvest, 114 georeferenced grain samples were obtained manually. Using spectral exploratory analysis, we calculated narrow-band physiological spectral indices-normalized difference spectral index (NDSI) and ratio spectral index (RSI)-from every single hyperspectral mosaic using complete two by two combinations of wavelengths. We applied two methods for the multi-temporal hyperspectral exploratory analysis: (a) Temporal Principal Component Analysis (tPCA) on wavelengths across all images and (b) the integration of vegetation indices over time based on area under the curve (AUC) calculations. For GY, the best R2 (0.32) were found using both the spectral (NDSI-Ri, 750 to 840 nm and Rj, ±720-736 nm) and the multi-temporal AUC exploratory analysis (EVI and OSAVI through AUC) methods. For GPC, all exploratory analysis methods tested revealed (a) a low to very low coefficient of determination (R2 ≤ 0.21), (b) a relatively low overall prediction error (RMSE: 0.45-0.49%), compared to results from other literature studies, and (c) that the spectral exploratory analysis approach is slightly better than the multi-temporal approaches, with early season NDSI of 700 with 574 nm and late season NDSI of 707 with 523 nm as the best indicators. Using residual maps from the regression analyses of NDSIs and GPC, we visualized GPC within-field variability and showed that up to 75% of the field area could be mapped with relatively good predictability (residual class: -0.25 to 0.25%), therefore showing the potential of remote sensing imagery to capture the within-field variation of GPC under conventional agricultural practices.