Prediction of water retention of soils from the humid tropics by the nonparametric k-nearest neighbor approach

Nonparametric approaches such as the k-nearest neighbor (k-NN) approach are considered attractive for pedotransfer modeling in hydrology; however, they have not been applied to predict water retention of highly weathered soils in the humid tropics. Therefore, the objectives of this study were: to apply the k-NN approach to predict soil water retention in a humid tropical region; to test its ability to predict soil water content at eight different matric potentials; to test the benefit of using more input attributes than most previous studies and their combinations; to discuss the importance of particular input attributes in the prediction of soil water retention at low, intermediate, and high matric potentials; and to compare this approach with two published tropical pedotransfer functions (PTFs) based on multiple linear regression (MLR). The overall estimation error ranges generated by the k-NN approach were statistically different but comparable to the two examined MLR PTFs. When the best combination of input variables (sand + silt + clay + bulk density + cation exchange capacity) was used, the overall error was remarkably low: 0.0360 to 0.0390 m(3) m(-3) in the dry and very wet ranges and 0.0490 to 0.0510 m(3) m(-3) in the intermediate range (i.e., -3 to -50 kPa) of the soil water retention curve. This k-NN variant can be considered as a competitive alternative to more classical, equation-based PTFs due to the accuracy of the water retention estimation and, as an added benefit, its flexibility to incorporate new data without the need to redevelop new equations. This is highly beneficial in developing countries where soil databases for agricultural planning are at present sparse, though slowly developing

Using soil organic matter fractions as indicators of soil physical quality

The objective of this study was to evaluate the use of chemical and physical fractions of soil organic matter (SOM), rather than SOM per se, as indicators of soil physical quality (SPQ) based on their effect on aggregate stability (AS). Chemically extracted humic and fulvic acids (HA and FA) were used as chemical fractions, and heavy and light fractions (HF and LF) obtained by density separation as physical fractions. The analyses were conducted on medium-textured soils from tropical and temperate regions under cropland and pasture. Results show that soil organic carbon (SOC), SOM fractions and AS appear to be affected by land use regardless of the origin of the soils. A general separation of structurally stable and unstable soils between samples of large and small SOC content, respectively, was observed. SOM fractions did not show a better relationship with AS than SOC per se. In both geographical regions, soils under cropland showed the smallest content of SOC, HA and carbon concentration in LF and HF, and the largest HF/LF ratio (proportion of the HF and LF in percent by mass of bulk soil). With significant associations between AS and SOC content (0.79**), FA/SOC (r = -0.83**), HA/FA (r = 0.58**), carbon concentration of LF (r = 0.69**) and HF (r = 0.70**) and HF/LF ratio (r = 0.80**), cropland showed lowest AS. These associations indicate that SOM fractions provide information about differences in SOM quality in relation to AS and SPQ of soils from tropical and temperate regions under cropland and pasture

Pattern Spectra from Different Component Trees for Estimating Soil Size Distribution

We study the pattern spectra in context of soil structure analysis. Good soil structure is vital for sustainable crop growth. Accurate and fast measuring methods can contribute greatly to soil management decisions. However, the current in-field approaches contain a degree of subjectivity, while obtaining quantifiable results through laboratory techniques typically involves sieving the soil which is labour- and time-intensive. We aim to replace this physical sieving process through image analysis, and investigate the effectiveness of pattern spectra to capture the size distribution of the soil aggregates. We calculate the pattern spectra from partitioning hierarchies in addition to the traditional max-tree. The study is posed as an image retrieval problem, and confirms the ability of pattern spectra and suitability of different partitioning trees to re-identify soil samples in different arrangements and scales

Soil pH Mapping with an On-The-Go Sensor

Soil pH is a key parameter for crop productivity, therefore, its spatial variation should be adequately addressed to improve precision management decisions. Recently, the Veris pH Manager™, a sensor for high-resolution mapping of soil pH at the field scale, has been made commercially available in the US. While driving over the field, soil pH is measured on-the-go directly within the soil by ion selective antimony electrodes. The aim of this study was to evaluate the Veris pH Manager™ under farming conditions in Germany. Sensor readings were compared with data obtained by standard protocols of soil pH assessment. Experiments took place under different scenarios: (a) controlled tests in the lab, (b) semicontrolled test on transects in a stop-and-go mode, and (c) tests under practical conditions in the field with the sensor working in its typical on-the-go mode. Accuracy issues, problems, options, and potential benefits of the Veris pH Manager™ were addressed. The tests demonstrated a high degree of linearity between standard laboratory values and sensor readings. Under practical conditions in the field (scenario c), the measure of fit (r2) for the regression between the on-the-go measurements and the reference data was 0.71, 0.63, and 0.84, respectively. Field-specific calibration was necessary to reduce systematic errors. Accuracy of the on-the-go maps was considerably higher compared with the pH maps obtained by following the standard protocols, and the error in calculating lime requirements was reduced by about one half. However, the system showed some weaknesses due to blockage by residual straw and weed roots. If these problems were solved, the on-the-go sensor investigated here could be an efficient alternative to standard sampling protocols as a basis for liming in Germany

Estimating soil aggregate size distribution from images using pattern spectra

A method for quantifying aggregate size distribution from the images of soil samples is introduced. Knowledge of soil aggregate size distribution can help to inform soil management practices for the sustainable growth of crops. While current in-field approaches are mostly subjective, obtaining quantifiable results in a laboratory is labour- and time-intensive. Our goal is to develop an imaging technique for quantitative analysis of soil aggregate size distribution, which could provide the basis of a tool for rapid assessment of soil structure. The prediction accuracy of pattern spectra descriptors based on hierarchical representations from attribute morphology are analysed, as well as the impact of using images of different quality and scales. The method is able to handle greater sample complexity than the previous approaches, while working with smaller samples sizes that are easier to handle. The results show promise for size analysis of soils with larger structures, and minimal sample preparation, as typical of soil assessment in agriculture

Effects of long-term inorganic and organic fertilizations on the soil micro and macro structures of rice paddies

The soil structure of paddy soil is very dynamic from the aggregate to the pedon scale because of intensive anthropogenic management strategies. In this study, we tested the hypothesis that long-term inorganic and organic fertilizations can affect soil structure at different scales. Microstructure assessed by soil aggregates (3–5 mm in diameter) and macrostructure assessed by small soil cores (CoreS) (5 cm in diameter, 5 cm in height) and large soil cores (CoreL) (10 cm in diameter, 10 cm in height) were sampled from three long-term fertilization treatments, including no fertilizer (CK), application of inorganic fertilizer (NPK), and a combination of inorganic fertilizer and organic manure (NPKOM), established in 1982. They were scanned at two scales with two types of micro-computed tomography (micro-CT) and quantified using image analysis. Results showed that relative to CK treatment, long-term NPKOM fertilization increased soil organic C (SOC) by 28% and available water content (AWC) by 20%, but decreased soil bulk density by 0.2 g cm− 3 whereas NPK showed no difference. Soils under CK and NPK treatments exhibited an identical dense structure at both aggregate and core scales in which pores were mainly cracks resulting from shrink/swell processes, and showed no significant difference in porosity and size distribution of the CT-identified pores (> 3.7 μm). Compared with the CK treatment, the soil in the NPKOM treatment had greater intra- and inter-aggregate pores, and increased porosity by 58.3%, 144.9%, and 65.9% at aggregate, CoreS, and CoreL scales, respectively. These were attributed to the biopores formed from decayed roots, stubble, and organic manures as a result of increased yields and direct amendment of organic manure. Overall, this study demonstrates that organic fertilization can improve the physical qualities of paddy soils across different scales but inorganic fertilization in isolation does not