Ability to make yield prediction before harvest using satellite remote sensing is important in many aspects of agricultural decision-making. In this study, canopy reflectance band and different band ratios in form of vegetation indices (VI) with leaf area index (LAI) were used to generate remotely sensed pre-harvest empirical rice yield prediction models. LAI measurements, spectral data derived from two SPOT data acquired on August 24, 2008 and August 23, 2009 and observed rice yield were used as main inputs for rice yield modeling. Each remotely sensed factor was used separately and in combination with LAI to generate the models. The results showed that green spectral band, middle infra-red spectral band and green vegetation index (GVI) did not show sufficient capability as rice yield estimators while other inputs such as red spectral band, near infrared spectral band and vegetation indices that are algebraic ratios from these two spectral bands when used separately or in combined with leaf area index (LAI) produced high accurate rice yield estimation models. The validation process was carried out using two statistical tests; standard error of estimate and the correlation coefficient between modeled and predicted yield. The validation results indicated that using normalized difference vegetation index (NDVI) combined with leaf area index (LAI) produced the model with highest accuracy and stability during the two rice seasons. The generated models are applicable 90days after planting in any similar environmental conditions and agricultural practices

The mapping of soil nutrients is a key issue for numerous applications and research fields ranging from global changes to environmental degradation, from sustainable soil management to the precision agriculture concept. The characterization, modeling and mapping of soil properties at diverse spatial and temporal scales are key factors required for different environments. This paper is focused on the use and comparison of soil chemical analyses, Visible near infrared and shortwave infrared VNIR-SWIR spectroscopy, partial least-squares regression (PLSR), Ordinary Kriging (OK), and Landsat-8 operational land imager (OLI) images, to inexpensively analyze and predict the content of different soil nutrients (nitrogen (N), phosphorus (P), and potassium (K)), pH, and soil organic matter (SOM) in arid conditions. To achieve this aim, 100 surface samples of soil were gathered to a depth of 25 cm in the Wadi El-Garawla area (the northwest coast of Egypt) using chemical analyses and reflectance spectroscopy in the wavelength range from 350 to 2500 nm. PLSR was used firstly to model the relationship between the averaged values from the ASD spectroradiometer and the available N, P, and K, pH and SOM contents in soils in order to map the predicted value using Ordinary Kriging (OK) and secondly to retrieve N, P, K, pH, and SOM values from OLI images. Thirty soil samples were selected to verify the validity of the results. The randomly selected samples included the spatial diversity and characteristics of the study area. The prediction of available of N, P, K pH and SOM in soils using VNIR-SWIR spectroscopy showed high performance (where R² was 0.89, 0.72, 0.91, 0.65, and 0.75, respectively) and quite satisfactory results from Landsat-8 OLI images (correlation R² values 0.71, 0.68, 0.55, 0.62 and 0.7, respectively). The results showed that about 84% of the soils of Wadi El-Garawla are characterized by low-to-moderate fertility, while about 16% of the area is characterized by high soil fertility.

The development of the agricultural sector is considered the backbone of sustainable development in Egypt. While the developing countries of the world face many challenges regarding food security due to rapid population growth and limited agricultural resources, this study aimed to assess the soils of Sidi Barrani and Salloum using multivariate analysis to determine the land capability and crop suitability for potential alternative crop uses, based on using principal component analysis (PCA), agglomerative hierarchical cluster analysis (AHC) and the Almagra model of MicroLEIS. In total, 24 soil profiles were dug, to represent the geomorphic units of the study area, and the soil physicochemical parameters were analyzed in laboratory. The land capability assessment was classified into five significant classes (C1 to C5) based on AHC and PCA analyses. The class C1 represents the highest capable class while C5 is assigned to lowest class. The results indicated that about 7% of the total area was classified as highly capable land (C1), which is area characterized by high concentrations of macronutrients (N, P, K) and low soil salinity value. However, about 52% of the total area was assigned to moderately high class (C2), and 29% was allocated in moderate class (C3), whilst the remaining area (12%) was classified as the low (C4) and not capable (C5) classes, due to soil limitations such as shallow soil depth, high salinity, and increased erosion susceptibility. Moreover, the results of the Almagra soil suitability model for ten crops were described into four suitability classes, while about 37% of the study area was allocated in the highly suitable class (S2) for wheat, olive, alfalfa, sugar beet and fig. Furthermore, 13% of the area was categorized as highly suitable soil (S2) for citrus and peach. On the other hand, about 50% of the total area was assigned to the marginal class (S4) for most of the selected crops. Hence, the use of multivariate analysis, mapping land capability and modeling the soil suitability for diverse crops help the decision makers with regard to potential agricultural development.

Drought is an environmental abiotic stress that diminishes wheat production worldwide. In the present study, we evaluated fifty bread wheat genotypes (arranged in alpha lattice design) under two main water regimes, water-deficit (two surface irrigations) and well-watered (four irrigations), at different sites in two consecutive cropping seasons, 2019/20 and 2020/21. To identify the drought-tolerant genotypes, utilized several selection/phenotyping criteria, including agronomic traits, e.g., grain yield (GY) and yield components (SM); physiological parameters such as canopy temperature (CT), leaf transpiration rate (TRN), intercellular CO₂ concentration (INCO); spectral reflectance indices, e.g., Leaf Chlorophyll Index (LCI), curvature index (CI), and normalized difference vegetation index (NDVI); and stress tolerance indices (STI) were determined concurrently with the grain yield. The results revealed significant differences (p ≤ 0.01) among the environments, genotypes, and their interaction for grain yield (GY), days to heading (DH), days to maturity (DM), grain filling period (GFP), grain filling rate (GFR), Normalized difference vegetation index (NDVI), plant height (PH), and spikes per square meter (SM). The genotype plus genotype by environment (GGE) and genotype by yield*trait (GYT) biplot techniques indicated that Genotype 37 (Sakha 95) and Genotype 45 performed best under well-watered and water-deficit environments. Furthermore, the same genotypes were the best from the genotype by stress tolerance indices (GSTI) approach view. Genotype 37 (Sakha 95) was superior to the GYT selection method, with physiological parameters and spectral reflectance indices. Likewise, we can identify this genotype as low-water-tolerant based on GSTI, GYT, and SRI results and recommend involving it in the drought breeding program.