The Agricultural Policy/Environmental eXtender (APEX) model was developed by the Blackland Research and Extension Center in Temple, Texas. APEX is a flexible and dynamic tool that is capable of simulating a wide array of management practices, cropping systems, and other land uses across a broad range of agricultural landscapes, including whole farms and small watersheds. The model can be configured for novel land management strategies, such as filter strip impacts on pollutant losses from upslope crop fields, intensive rotational grazing scenarios depicting movement of cows between paddocks, vegetated grassed waterways in combination with filter strip impacts, and land application of manure removed from livestock feedlots or waste storage ponds. A description of the APEX model is provided, including an overview of all the major components in the model. Applications of the model are then reviewed, starting with livestock manure and other management scenarios performed for the National Pilot Project for Livestock and the Environment (NPP), and then continuing with feedlot, pesticide, forestry, buffer strip, conservation practice, and other management or land use scenarios performed at the plot, field, watershed, or regional scale. The application descriptions include a summary of calibration and/or validation results obtained for the different NPP assessments as well as for other APEX simulation studies. Available APEX GIS-based or Windows-based interfaces are also described, as are forthcoming improvements and additional research needs for the model.

No abstract

No abstract

No abstract

No abstract

No abstract

The socio-economic development in Egypt is based on land resources. Recently, the Egyptian government is interested in developing low desert zone areas which are located between the recent Nile flood plain and the limestone plateau, from the east and west sides, and represent an important source of aggregate materials. Therefore, this study was carried out to investigate the potentiality of El-Galaba basin soils which are located in the western part of the Aswan Governorate and are characterized by Wadi El-Kubbaniya for the horizontal agricultural expansion and their optimum agricultural use. The investigated area was remotely sensed to identify the landscape and its land resources. Terrain units were identified using draped Landsat 8 satellite image over Digital Terrain Model (DTM) to express the landscape and the associated soil mapping units. Fifteen mapping units were identified and grouped. Land capability evaluation was performed using Cervatana capability model. The results of capability modeling revealed about 3.33% of land with good use capability, 76.06% land with moderate use capability, and 0.08% marginal or non-productive land. The main capability limitations were soil and erosion risks. The Almagra model was used to produce the optimum cropping pattern and limitations of soil units. Matching the crop requirements with soil characteristics, optimum cropping pattern was obtained for wheat, corn, melon, potatoes, sunflower, sugar beet, Alfalfa, peach, citrus, and olive. The results of the study revealed the potentiality of El-Galaba basin for agricultural uses. Ó 2015 Authority for Remote Sensing and Space Sciences. Production and hosting by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Assessing soil quality is considered one the most important indicators to ensure planned and sustainable use of agricultural lands according to their potential. The current study was carried out to develop a spatial model for the assessment of soil quality, based on four main quality indices, Fertility Index (FI), Physical Index (PI), Chemical Index (CI), and Geomorphologic Index (GI), as well as the Geographic Information System (GIS) and remote sensing data (RS). In addition to the GI, the Normalized Difference Vegetation Index (NDVI) parameter were added to assess soil quality in the study area (western part of Matrouh Governorate, Egypt) as accurately as possible. The study area suffers from a lack of awareness of agriculture practices, and it depends on seasonal rain for cultivation. Thus, it is very important to assess soil quality to deliver valuable data to decision makers and regional governments to find the best ways to improve soil quality and overcome the food security problem. We integrated a Digital Elevation Model (DEM) with Sentinel-2 satellite images to extract landform units of the study area. Forty-eight soil profiles were created to represent identified geomorphic units of the investigated area. We used the model builder function and a geostatistical approach based on ordinary kriging interpolation to map the soil quality index of the study area and categorize it into different classes. The soil quality (SQ) of the study area, classified into four classes (i.e., high quality (SQ2), moderate quality (SQ3), low quality (SQ4), and very low quality (SQ5)), occupied 0.90%, 21.87%, 22.22%, and 49.23% of the total study area, respectively. In addition, 5.74% of the study area was classified as uncultivated area as a reference. The developed soil quality model (DSQM) shows substantial agreement (0.67) with the weighted additive model, according to kappa coefficient statics, and significantly correlated with land capability R2 (0.71). Hence, the model provides a full overview of SQ in the study area and can easily be implemented in similar environments to identify soil quality challenges and fight the negative factors that influence SQ, in addition to achieving environmental sustainability.