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The fragmented nature of arable landscapes and diverse cropping patterns often thwart the precise mapping of crop types. Recent advances in remote-sensing technologies and data mining approaches of...

Management practices influence soil properties in both the seasonal short-term and multi-year long-term. Seasonal changes in soil compaction were assessed at the Ohio Triplett-Van Doren No-Tillage Experimental Plots, which have been operational for more than 55 years. Soil penetration resistance (PR), carbon, and nitrogen were measured from plots under plow tillage (PT), minimum tillage (MT), and no-tillage (NT) management in Hoytville and Wooster, Ohio. Water retention curves (WRC) and pore size distribution (PSD) were evaluated at the Wooster site only. Significant tillage treatment effects on PR were observed at both sites throughout the soil profile, but varied by sampling time. In the silt loam, NT systems showed higher PR than plow treatments until below the plow depth of 20 cm, where the presence of a plow pan was seen under the PT treatment. During the summer and fall these subsurface treatment effects were no longer present. The tillage effects were more pronounced in the silty clay loam soil, where NT plots were more compacted than MT and PT treatments throughout the profile. This was observed in all the spring, summer and fall sampling surveys. There were significant tillage treatment effects (p

Despite the truth that the agricultural land use planning exercises have so far covered small and fragmented part of the African continent, the involvement of farmers who are among the direct beneficiaries of the outputs have been limited. This work demonstrates the contributions of farmers on the land use planning process for rice production in Kilombero Valley, Tanzania. Analytic hierarchic process (AHP) was used to assign scores of comparative importance of attributes for a suitable land for rice production. Scoring was done by three groups: farmers, extension staff, and joint group comprising both farmers and extension staff. Joint group scores were considered more refined as they were generated by discussions and consensus between the two groups. Results showed that the three groups sequentially ranked the attributes the same. However, the attributes actual scores were different. The farmers’ scores were consistently close to the joint group’s scores compared to the extension staff group. The closeness suggests superiority and consistence of farmers’ perceptions of importance of the identified attributes used for this land use planning exercise. Thus, this study recommends more involvement of farmers in agricultural land use planning process for better and sustainable land use planning outputs. Key words: Kilombero Valley, rice, land use planning, Tanzania, Analytic Hierarchic Process.

Inadequacy of spatial soil information is one of the limiting factors to making evidence-based decisions to improve food security and land management in the developing countries. Various digital soil mapping (DSM) techniques have been applied in many parts of the world to improve availability and usability of soil data, but less has been done in Africa, particularly in Tanzania and at the scale necessary to make farm management decisions. The Kilombero Valley has been identified for intensified rice production. However the valley lacks detailed and up-to-date soil information for decision-making. The overall objective of this study was to develop a predictive soil map of a portion of Kilombero Valley using DSM techniques. Two widely used decision tree algorithms and three sources of Digital Elevation Models (DEMs) were evaluated for their predictive ability. Firstly, a numerical classification was performed on the collected soil profile data to arrive at soil taxa. Secondly, the derived taxa were spatially predicted and mapped following SCORPAN framework using Random Forest (RF) and J48 machine learning algorithms. Datasets to train the model were derived from legacy soil map, RapidEye satellite image and three DEMs: 1 arc SRTM, 30 m ASTER, and 12 m WorldDEM. Separate predictive models were built using each DEM source. Mapping showed that RF was less sensitive to the training set sampling intensity. Results also showed that predictions of soil taxa using 1 arc SRTM and 12 m WordDEM were identical. We suggest the use of RF algorithm and the freely available SRTM DEM combination for mapping the soils for the whole Kilombero Valley. This combination can be tested and applied in other areas which have relatively flat terrain like the Kilombero Valley.

International audience; Digital Soil Mapping and Assessment (DSMA) has progressed from challenging traditional soil science paradigms, through small scale prototyping, to large-scale implementation capturing quantitative measures of soil attributes and functions. This paper considers the future for DSMA in the context of a highly uncertain world where high-quality knowledge of soil dynamics will be important for responding to the challenges of sustainability. Irrespective of whether the need is for survival, increased productivity or broadening the services provided from land management, or simply securing the soil itself, we see DSMA as a fundamental approach and essential tool. With a broadening need and a strong foundation in the practice of DSMA now in place, the theory, tools and technology of DSMA will grow significantly. We explore expected changes in covariate data, the modelling process, the nature of base data generation and product delivery that will lead to tracking and forecasting a much wider range of soil attributes and functions at finer spatial and temporal resolutions over larger areas, particularly globally. Equally importantly, we expect the application and impact of DSMA to broaden and be used, directly and collaterally, in the analysis of land management issues in coming decades. It has the capacity to provide the background to a soil and landscape ‘digital twin’ and the consequent transformation in monitoring and forecasting the impacts of land management practices. We envision the continued growth of DSMA skills amongst soil scientists and a much broader community of practice involved in developing and utilizing DSMA products and tools. Consequently, there will be a widening and deepening role of public-private partnerships in this development and application.

As urbanization expands, interest in understanding soil properties and functions in urban environments has also increased. Urban soils play an important role in the global carbon (C) cycle. Due to natural and human influences on various forms of C, quantifying soil inorganic carbon (SIC) becomes critical in urban areas. In this study, the suitability of diffuse reflectance mid-infrared Fourier-transform spectroscopy (mid-DRIFTS) to rapidly estimate calcite and dolomite concentrations was tested. Also, the effectiveness of HCl acid pre-treatment to remove carbonates to enable proper estimation of soil organic carbon (SOC) in urban soils was evaluated. Specific mid-infrared frequencies known to be associated with carbonates, i.e. dolomite and calcite, were identified, and specific peak areas were measured and calibrated with results obtained with the volumetric calcimeter method. Multiple linear regression was used to estimate total carbonates with peaks areas at 2930 cm−1, 2515 cm−1 and 730 cm−1 resulting in a 3.72% root mean squared error of an independent validation set (RMSEV). Greater prediction accuracy of calcite (875 cm−1) and dolomite (875 cm−1 and 730 cm−1) were achieved using single peaks in bivariate relationships resulting in RMSEV of 1.27% and 4.22%, respectively. Soil samples were treated with 1 M HCl for 24 h to remove carbonates, and total carbonate, dolomite, and calcite contents were estimated again using both volumetric calcimeter and mid-DRIFTS. The reduction in carbonate concentrations post-acid treatment was considerable (97% for dolomite, and 96% for calcite and total carbonates), but was not complete. Thus, this could compromise quantification of other soil carbon fractions such as total SOC. Using mid-DRIFTS peak areas of CO3−2 functional groups represents a robust alternative for rapid, fast, and low-cost laboratory measurements of soil carbonates, and this high-throughput approach may be used in assays aiming to minimize carbonate interference in SOC estimations.

The objectives of this study were to evaluate several spectral indices for estimating soil tillage practices and determining crop residue cover using multispectral and Hyperspectral satellite image...

Synthetic gypsum, a by-product of electricity generation, is used as a soil amendment to overcome water ponding, improve soil and water quality, improve field conditions to support farm equipment, and reduce the variability of crop yield in no-till fields by improving hydrology. Gypsum is a source of soluble calcium (Ca) that improves physical properties of the soil by promoting clay aggregation, thereby increasing water infiltration rates and movement through the soil profile. Undisturbed soil samples from Brookston and Celina soils in Ohio, USA were collected to a depth of 75 cm in agricultural fields treated with gypsum for 0, 4, and 12 years to determine changes in chemical and physical properties. Gypsum applications increased exchangeable Ca and Ca: Mg ratios, and promoted clay flocculation and improved soil structure. Mean weight diameter of aggregates increased with gypsum treatment at most depths in both soils. Micromorphological analysis showed variations in porosity (ɸ), pore size distribution, pore shape and aggregate size related to gypsum treatment, soil, and soil depth. There were no consistent responses to years of gypsum application. Gypsum treated soils had higher porosity than untreated soils in all depths

Since the first documented soil survey in Tanzania by Milne (J Ecol 35:192–265, 1936), a number of other soil inventory exercises at different scales have been made. The main challenge has been the fragmented nature of the often outdated detailed soil maps and small-scale less-informative country-wide soil maps. Recent advances in information and computational technology have created vast potential to collect, map, harness, communicate and update soil information. These advances present favorable conditions to support the already popular shift from qualitative (conventional) to quantitative (digital) soil mapping (DSM). In this study, two decision tree machine learning algorithms, J48 and Random Forest (RF), were applied to digitally predict k-means numerically classified soil clusters to update a soil map produced in 1959. Predictors were derived from 1 arc SRTM digital elevation data and a 5 m RapidEye satellite image. J48 and RF predicted the soil units of the legacy maps with greater detail. However, RF showed superiority for predicting clusters J48 could not predict and for showing higher pixel contiguity. No significant difference (P = 0.05) was observed between the soil properties of the predicted soil clusters and the actual field validation points. Young soils (Entisols and Inceptisols) were found to occupy about 56 % of the study site’s 30,000 ha followed by Alfisols, Mollisols and Vertisols at 31, 9 and 4 %, respectively. This study demonstrates the usefulness of DSM techniques to update conventionally prepared legacy maps to offer soil information at improved detail to agricultural land use planners and decision makers of Tanzania to make evidence-based decisions for climate-resilient agriculture and other land uses.