EXTENT OF MINERALIZATION ORGANIC FERTILIZER ON SALT AFFECTED SOIL AND THAT IMPLEMENTATION ON TOMATO

Mineralization of organic fertilizers in saline soil is determined by the level of soil salinity. The higher levels of soil salinity, the lower the ability of organic matter mineralization. Evaluation mineralization levels assessed by the content of N, P, K, C-org in organic fertilizer. Research objectives were to assess the ability of the various formulas of mineralization of organic fertilizer to provide nutrients and suppress soil salinity. Nutritional NPK fertilizers are classified by grade. The results showed that the formula with high-grade organic fertilizer was obtained from a mixture of manure, compost, guano, and straw. High-grade organic fertilizer is not always effective as the controlling soil salinity and aggregate stability, but can increase the CEC and the availability of N, N Ammonium inhibits volatilization, decrese soil EC, but soil pH was increased. Mineralization rate of organic fertilizer on clay-textured soil (Rungkut and Sedati) more slowly than sandy soil (Buduran). Keywords: grade, mineralization, NPK, organic fertilizers, soil salinit

Stochastic modeling of soil salinity

A minimalist stochastic model of primary soil salinity is proposed, in which the rate of soil salinization is determined by the balance between dry and wet salt deposition and the intermittent leaching events caused by rainfall events. The long term probability density functions of salt mass and concentration are found by reducing the coupled soil moisture and salt mass balance equation to a single stochastic differential equation driven by multiplicative Poisson noise. The novel analytical solutions provide insight on the interplay of the main soil, plant and climate parameters responsible for long-term soil salinization. In particular, they show the existence of two distinct regimes, one where the mean salt mass remains nearly constant (or decreases) with increasing rainfall frequency, and another where mean salt content increases markedly with increasing rainfall frequency. As a result, relatively small reductions of rainfall in drier climates may entail dramatic shifts in long-term soil salinization trends, with significant consequences e.g. for climate change impacts on rain-fed agricultur

Implications of land and water degradation on food security

Land degradation / Erosion / Water pollution / Sedimentation / Groundwater depletion / Salt water intrusion / Soil salinity / Urbanization / Wetlands

Managing the Economics of Soil Salinity

Saline soils result in decreased crop growth and yield with the potential for losing productive farm land. Enterprise budget analysis was extended to include the fixed costs of installing tile drainage to manage soil salinity in the Red River Valley of North Dakota for corn, soybeans, wheat, sugar beets, and barley. Installing tile drainage to manage soil salinity decreased per acre crop profitability from 19-49% due to the large upfront capital investment of tile drainage. These losses can be decreased to zero with more consistent and predictable yields from tile drainage in the intermediate to long run. With no salinity management lost revenues were estimated to be $150 million due to 1.2 million acres of slightly saline soils and 275,000 acres of moderate soil salinity.Crop Production/Industries, Land Economics/Use,

How to manage salinity in irrigated lands: a selective review with particular reference to irrigation in developing countries

Irrigation management / Irrigable land / Soil salinity / Water use efficiency / Soil degradation / Irrigated farming / Policy making / Developing countries

ECONOMICS OF AGROFORESTRY PRODUCTION IN IRRIGATED AGRICULTURE

A dynamic optimization model for agroforestry management is developed where tree biomass and soil salinity evolve over time in response to harvests and irrigation water quantity and quality. The model is applied to agroforestry production in the San Joaquin Valley of California. Optimal water applications are at first increasing in soil salinity, then decreasing, while the harvest decision is relatively robust to changes in most of the underlying economic and physical parameters. Drainwater reuse for agroforestry production also appears promising: both net reuse volumes and the implied net returns to agroforestry are substantial.Resource /Energy Economics and Policy,

Using nonlinear geostatistical models for soil salinity and yeild management

2013 Fall.Includes bibliographical references.Crop production losses associated with soil salinity on irrigated lands are significant. The genetic complexity of crops with regards to salt tolerance has limited the success of improving salt tolerance through conventional breeding programs. In the meantime, land reclamation and leaching can be expensive and sometimes impractical when fresh water sources are scarce or not readily available. This research introduces a geostatistical approach for the management of crop yield under current soil salinity conditions. It uses three nonlinear geostatistical models - disjunctive kriging (DK), indicator kriging (IK), and probability kriging (PK) - to manage soil salinity and crop yield. The nonlinear models were applied to selected irrigated fields in a study area located in the south eastern part of the Arkansas River Basin in Colorado where soil salinity is a problem in some areas. The overall objectives of this research are: 1) estimate soil salinity in irrigated fields using nonlinear gestatistical models; 2) develop conditional probability (CP) maps that divide each field into zones with different soil salinity levels; 3) estimate the expected yield potential (YP) for several crops at different zones in fields under multiple soil salinity thresholds; 4) evaluate the performance of the nonlinear geostatistical models in developing the interpolated and CP maps provide guidance to farmers and researchers by considering the output of this research as input for precision management of agriculture; and 5) provide guidance to farmers and decision makers in precision management of agriculture. The three nonlinear geostatistical models DK, IK, and PK were used to develop CP maps based on soil salinity thresholds for different crops. These CP maps were compared with actual yield data taken while conducting a soil salinity survey for two fields cultivated with alfalfa and corn. The CP maps divide each field of interest into zones with different probabilities to reach a specific YP for a given crop at a specific soil salinity threshold. Different crops were selected to represent the dominant crops grown in the study area: alfalfa, corn, sorghum, and wheat. Six fields were selected to represent the range of soil salinity levels in the area. Soil salinity data were collected in the fields using an EM-38 and the location of each soil salinity sample point was determined using a GPS unit. Datasets of soil salinity collected in irrigated fields were used to generate the CP maps and to evaluate different scenarios of the expected YP% of several crops at multiple soil salinity thresholds. These datasets were selected to represent a wide range of soil salinity conditions in order to be able to evaluate a wide variety of crops (larger set of crops than those grown in the study area) according to their soil salinity tolerances. Yield data were collected at the same fields to compare the actual data with that estimated by the models. The crops were used for evaluation were selected based on two criteria: dominant in the study area, and represent high, moderate, and low soil salinity tolerances. Different scenarios of crops and salinity levels were evaluated. Semivariograms were constructed for each scenario to represent the different classes of percent yield potential based on soil salinity thresholds of each crop. The results of this research show the nonlinear geostatistical models are efficient in assessing the impact of soil salinity on the spatial variability yield productivity. The comparison of the actual yield data with the estimated yield from the three models shows good agreement where most of the yield samples were located at the appropriate zones estimated with the models. The IK and PK models generated very similar estimates for each of the zones. However, the zones generated by both of these models are slightly different to the zones generated using the DK model. Wheat and sorghum show the highest expected yield potential based on the different soil salinity conditions that were evaluated. Expected net revenue for alfalfa and corn are the highest under the different soil salinity conditions that were evaluated. The CP maps generated using the DK technique give an accurate characterization and quantification of the different zones of the fields. Upon the knowledge of the YP% of different areas, a management decision action can be taken to manage the productivity of a field by selecting another crop or adjusting the inputs such as fertilizer, seeding rates and herbicides in low productivity areas. The information provided by the models about the variability and hotspots can be used for the precision management of agricultural resources. The IK model can be used to generate guidance maps that divide each field into areas of expected percent yield potential based on soil salinity thresholds for different crops. Zones of uncertainty can be quantified by IK and used for risk assessment of the percent yield potential

Quantitative Estimation of Saline-Soil Amelioration Using Remote-Sensing Indices in Arid Land for Better Management

Soil salinity and sodicity are significant issues worldwide. In particular, they represent the most dominant types of degraded lands, especially in arid and semi-arid regions with minimal rainfall. Furthermore, in these areas, human activities mainly contribute to increasing the degree of soil salinity, especially in dry areas. This study developed a model for mapping soil salinity and sodicity using remote sensing and geographic information systems (GIS). It also provided salinity management techniques (leaching and gypsum requirements) to ameliorate soil and improve crop productivity. The model results showed a high correlation between the soil electrical conductivity (ECe) and remote-sensing spectral indices SIA, SI3, VSSI, and SI9 (R-2 = 0.90, 0.89, 0.87, and 0.83), respectively. In contrast, it showed a low correlation between ECe and SI5 (R-2 = 0.21). The salt-affected soils in the study area cover about 56% of cultivated land, of which the spatial distribution of different soil salinity levels ranged from low soil salinity of 44% of the salinized cultivated land, moderate soil salinity of 27% of salinized cultivated land, high soil salinity of 29% of the salinized cultivated land, and extreme soil salinity of 1% of the salinized cultivated land. The leaching water requirement (LR) depths ranged from 0.1 to 0.30 m ha(-1), while the gypsum requirement (GR) ranged from 0.1 to 9 ton ha(-1)

Spatial and Temporal Variation of Soil Salinity During Dry and Wet Seasons in the Southern Coastal Area of Laizhou Bay, China

260-270The southern coastal area of Laizhou Bay is subjected to severe soil salinization due to saline groundwater. The degree of spatial variability is strongly affected by seasonal changes during an annual cycle. In this paper, the spatio-temporal variability of soil salinity in Laizhou Bay, China, was examined to ascertain the current situation of soil salinization in the study area and to reveal the characteristics of seasonal variation of soil salinity. The classical statistical methods and geostatistical methods were applied to soil salinity data collected from four soil layers, i.e., 0-30, 30-60, 60-90, and 0-100 cm, during summer and autumn in 2014. The results indicated that the variation of soil salinity of all the soil layers in summer and autumn was moderate. The soil salinity in the 0-30 cm layer showed a moderate spatial autocorrelation, whereas the spatial autocorrelations of soil salinity in other layers were strong. The overall spatial distribution of soil salinity showed a clear banding distribution and the degree of salinization in the eastern area was lower than that in the western and northern regions.A high ratio of evaporation/precipitation is one of the important reasons for the soil salinity in July is significantly higher than that in November. The rank of soil salinity under different land-use types was: salt pan > orchard > weeds > soybean > woods > cotton > maize > ginger > sweet potato. The research findings can provide theoretical guidance for accurate assessment and soil partition management of regional soil salinization

Prospects for productive use of saline water in West Asia and North Africa

Water quality / Salinity / Soil salinity / Irrigated farming / Crop production / Feed crops / Fodder / Poverty / Public policy / West Asia / North Africa / Egypt / Jordan / Syria / Tunisia