The assessment of spatial distribution of soil salinity risk using neural network.

Soil salinity in the Aral Sea Basin is one of the major limiting factors of sustainable crop production. Leaching of the salts before planting season is usually a prerequisite for crop establishment and predetermined water amounts are applied uniformly to fields often without discerning salinity levels. The use of predetermined water amounts for leaching perhaps partly emanate from the inability of conventional soil salinity surveys (based on collection of soil samples, laboratory analyses) to generate timely and high-resolution salinity maps. This paper has an objective to estimate the spatial distribution of soil salinity based on readily or cheaply obtainable environmental parameters (terrain indices, remote sensing data, distance to drains, and long-term groundwater observation data) using a neural network model. The farm-scale (∼15 km(2)) results were used to upscale soil salinity to a district area (∼300 km(2)). The use of environmental attributes and soil salinity relationships to upscale the spatial distribution of soil salinity from farm to district scale resulted in the estimation of essentially similar average soil salinity values (estimated 0.94 vs. 1.04 dS m(-1)). Visual comparison of the maps suggests that the estimated map had soil salinity that was uniform in distribution. The upscaling proved to be satisfactory; depending on critical salinity threshold values, around 70-90% of locations were correctly estimated.

Nitrogen fixation by Elaeagnus angustifolia in the reclamation of degraded croplands of Central Asia.

Extensive degradation of irrigated croplands, due to increasing soil salinity and depletion of soil nutrient stocks, is a major problem in Central Asia (CA), one of the largest irrigated areas in the world. To assess the potential for improving the productive capacity of degraded lands by afforestation, we examined N(2) fixation of Elaeagnus angustifolia L. in mixed plantations with non-fixing Populus euphratica Oliv. and Ulmus pumila L. Fixation of N(2) was quantified by the (15)N natural abundance technique based on both foliar and whole-plant sampling during five consecutive growing seasons. Despite elevated root-zone soil salinity (6-10 dS m(-1)) and deficiency in plant-available P (4-15 mg kg(-1)), N(2) fixation (%Ndfa) increased from an initial value of 20% to almost 100% over 5 years. Within each growing season, %Ndfa steadily increased and peaked in the fall. Annual N(2) fixation, determined using foliar delta(15)N, initially averaged 0.02 Mg ha(-1), peaked at 0.5 Mg ha(-1) during the next 2 years and thereafter stabilized at 0.3 Mg ha(-1). Estimates based on whole-plant delta(15)N were <10% lower than those based on foliar delta(15)N. The increase in plant-available soil N was significantly higher in E. angustifolia plots than in P. euphratica and U. pumila plots. Increases in the concentrations of organic C (19%), total N (21%) and plant-available P (74%) in the soil were significant irrespective of tree species. This improvement in soil fertility is further evidence that afforestation with mixed-species plantations can be a sustainable land use option for the degraded irrigated croplands in CA.

Land-cover change trajectories in northern Ghana.

Land-cover change trajectories are an emergent property of complex human-environment systems such as the land-use system. An understanding of the factors responsible for land change trajectories is fundamental for land-use planning and the development of land-related policies. The aims of this study were to characterize and identify the spatial determinants of agricultural land-cover change trajectories in northern Ghana. Land-cover change trajectories were defined using land-cover maps prepared from Landsat Thematic Mapper dataset acquired in 1984, 1992, and 1999. Binary logistic regression was used to model the probability of observing the trajectories as a function of spatially explicit biophysical and socioeconomic independent variables. Population densities generally increased along the continuum of land-use intensity, whereas distance from market and roads generally decreased along this continuum. Apparently, roads and market serve as incentives for settlement and agricultural land use. An increase in population density is an important spatial determinant only for trajectories where the dominant change process is agricultural extensification. A major response to population growth is an increase in cultivation frequency around the main market. Agricultural intensification is highly sensitive to accessibility by roads. The increase in land-use intensity is also associated with low soil quality. These results suggest the need for policies to restore soil fertility for agricultural sustainability. The models also provide a means for identifying functional relationships for in-depth analyses of land-use change in Ghana.

Land evaluation for maize based on fuzzy set and interpolation.

The objective of this article is to apply fuzzy set and interpolation techniques for land suitability evaluation for maize in Northern Ghana. Land suitability indices were computed at point observations using the Semantic Import (SI) model, whereas spatial interpolation was carried out by block kriging. Interpolated land suitability shows a high correlation (R2 = 0.87) with observed maize yield at the village level. This indicates that land suitability is closely related to maize yield in the study area. Membership functions were further used to assess the degree of limitation of land characteristics to maize. Sixty percent of the data has membership functions ranging from 0.23 for ECEC to 1.00 for drainage. ECEC, organic C, and clay are the major constraints to maize yield. The use of the fuzzy technique is helpful for land suitability evaluation, especially in applications in which subtle differences in soil quality are of a major interest. Furthermore, the use of kriging that exploits spatial variability of data is useful in producing continuous land suitability maps and in estimating uncertainties associated with predicted land suitability indices.