CLASSIFICATION AND MAPPING OF LAND USE AND SOME SOIL PROPERTIES IN KIRŞEHIR PROVINCE, TURKEY

In this study, land use capabilities, land types and other soil properties of Kırşehir province were classified and analyzed. In the study, 1/25.000 scale digital soil maps obtained from the Ministry of Agriculture and Forestry (Turkey) were used. Numerical data were classified using Arc GIS 10.3.1 software, which is one of the GIS software. As a result of the research; In general, It was observed that IV. class lands were formed in the Kirsehir province IV.class lands were found to be 1658.3 km2 and it was determined that they cover 25% of the total area. It is seen that soil insufficiency is high in Kırşehir province due to slope and erosion damage. Soil insufficiency due to slope and erosion damage was found to be 3520.7 km2 and it was determined that 54% of the total area was exposed to this effect. It has been observed that the land type is generally composed of steppe, bare rocks and rubble. It was determined that the area formed by bare, rocks and debris is 1128.5 km2. It has been determined that the stony soil areas are 1094.2 km2. As a result of the study, classified map outputs related to land uses and some soil properties were obtained. It will be inevitable that this research will provide important database bases for other studies to be carried out in this region in the future.

Digital soil mapping of soil fertility index for agricultural lands in tropical environment

<p>Indicators of soil production function such as soil fertility index can potentially be a key decision tool in spatial planning for sustainable land management. The establishment of such soil fertility index requires basic soil properties which can be modelled for spatial mapping. The objective of this study was to take advantage of the soil legacy data of Benin to produce a digital soil map of soil fertility index at a national scale based on 8 soil properties (soil organic carbon matter, nitrogen, pH, exchangeable potassium, assimilable phosphorus, sum of base, cation exchange capacity and base saturation). Specific research aims were: (1) to model and develop digital soil maps; (2) to identify important factors influencing soil nutrients; (3) to establish soil fertility potentials using digital soil maps. For each soil property, modelling procedures involved the use of different covariates including soil type, topographic, bioclimatic and spectral data along with the comparative assessment of the Cubist and Quantile Random Forest model. Results revealed that apart from N and exchangeable K, significant models can be produced for most of the soil properties with R-square varying between 28% and 72% with the Quantile Random Forest presenting a more accurate prediction interval coverage probability. The analysis revealed that the distance to the nearest stream has strong predictive ability for all the soil properties along with the bioclimatic variables. Visualisation of the soil fertility map showed that most of the soils in Benin have low fertility level suggesting that the use of fertilizers and organic materials will be critical in sustaining crop productivity. A limited number of high and average fertility level soils were found in the low elevation areas of southern Benin and policy could advocate for their sole use for agriculture purpose as well as promote sustainable management practices.</p>

Designing better input support programs: Lessons from zinc subsidies in Andhra Pradesh, India

India has one of the largest agricultural input support programs in the world, delivered in the form of subsidies to farmers, raising concerns about its sustainability. This paper evaluates the performance of one such support, the micronutrient subsidy program in the state of Andhra Pradesh (AP) and presents a case for providing this support in the form of direct cash transfers. Under the program, key soil micronutrients- zinc, boron, and gypsum were distributed free of cost to farmers living in micronutrient-deficient areas, with identification and targeting managed entirely by the state. We survey 1621 farmers, 61 agriculture extension officers, and 78 agriculture input dealers to assess the efficacy of the program and to identify bottlenecks preventing effective targeting, with a focus on zinc. We find that use of non-subsidized zinc is high in AP, and awareness of benefits of zinc and physical access to input dealer shops are significant predictors of zinc use. We argue that the free provision of micronutrients may have created demand among farmers, but there is little justification to continue subsidizing such a program at such high rates or resorting to public distribution. We find that micronutrient procurement and distribution has become a burden on extension staff and crowds out the private sector. Our analysis shows that the subsidy can benefit more farmers if it is channeled through the network of private fertilizer dealers. We use administrative data on budgetary outlays and digital soil maps to suggest fiscal redistribution in the form of direct cash transfers that may ensure more effective targeting at a lower cost to the state.

A novel downscaling procedure for compositional data in the Aitchison geometry with application to soil texture data

In this work, we present a novel downscaling procedure for compositional quantities based on the Aitchison geometry. The method is able to naturally consider compositional constraints, i.e. unit-sum and positivity, accounting for the scale invariance and relative scale of these data. We show that the method can be used in a block sequential Gaussian simulation framework in order to assess the variability of downscaled quantities. Finally, to validate the method, we test it first in an idealized scenario and then apply it for the downscaling of digital soil maps on a more realistic case study. The digital soil maps for the realistic case study are obtained from SoilGrids, a system for automated soil mapping based on state-of-the-art spatial predictions methods.

Mobile Proximal Sensing with Visible and Near Infrared Spectroscopy for Digital Soil Mapping

The objective of this study was to estimate multiple soil property local regression models, confirm the accuracy of the predicted values using visible near-infrared subsurface diffuse reflectance spectra collected by a mobile proximal soil sensor, and show that digital soil maps predicted by multiple soil property local regression models are able to visualize empirical knowledge of the grower. The parent materials in the experimental fields were light clay, clay loam, and sandy clay loam. The study was conducted in Saitama Prefecture, Japan. To develop local regression models for the 30 chemical and 4 physical properties, a total of 231 samples were collected; to evaluate accuracy of prediction, 65 samples were collected. The local regression models were developed using 2nd derivative pretreatment by the Savitzky–Golay algorithm and partial least squares regression. The local regression models were evaluated using the coefficient of determination (R2), residual prediction deviation (RPD), range error ratio (RER), and the ratio of prediction error to interquartile range (RPIQ). The R2 accuracy of the 34 local regression models was 0.81 or higher. In the predicted values for 65 unknown samples, the local regression models could ‘distinguish between high and low’ for 3 of the 34 soil properties, but were ‘not useful’ as absolute quantitative values for the other 31 soil properties. However, it was confirmed that the predicted values followed the transition in measured values, and thus that the developed 34 regression models could be used for generating digital soil maps based on relative quantitative values. The grower changed the ridge direction in the field from east–west to north–south just looking at the digital soil maps.

GIS as A Tool for Expansive Soil Detection at Sulaymaniyah City

Geotechnical engineering like any other engineering field has to develop and cope with new technologies. This article intends to investigate the spatial relationships between soil’s liquid limit (LL), plasticity index (PI) and Liquidity index (LI) for particular zones of Sulaymaniyah City. The main objective is to study the ability to produce digital soil maps for the study area and determine regions of high expansive soil. Inverse Distance Weighting (IDW) interpolation tool within the GIS (Geographic Information System) program was used to produce the maps. Data from 592 boreholes for LL and PI and 245 boreholes for LI were used for this study. Layers were allocated into three depth ranges (1 to 2, 2 to 4 and 4 to 6) m. A total of 1396 observations were used for producing the maps for both LL and PI, and 371 data for LI. Based on the results, the IDW method gives reasonable predictions depending on the results of R2 and RMSE. The results also showed that the study area has relatively large zones of high expansive soil that must be taken into considerations before performing any construction activity. These maps are essential for helping geotechnical engineers in making decisions and visualizing soils’ behaviors.