Index for refining soil health assessment through multivariate approach under diverse agro-climatic zones in the Indo-Gangetic basin of Bihar.

A fundamental necessity in advancing sustainable crop production lies in the establishment of a reliable technique for assessing soil health. Soil health assessment is a challenge considering multiple interactions among dynamic indicators within various management strategies and agroecological contexts. Hence a study was conducted to determine the soil health variables, quantify the soil health index (SHI), and validate them with the productivity of rice (Oryza sativa L.)-wheat (Triticum aestivum L.) system for the Indo Gangetic basin of Bihar, India, under four contrasting agro-climatic zones (ACZ-I, II, IIIA & IIIB). For this study, 100 soil samples (0-15 cm) from each ACZ with a total of 400 soil samples were obtained for analyzing 20 soil health variables (soil physical, chemical, and biological properties). To identify SHI and important soil health variables, principal component analysis (PCA) was employed. Apart from specific variables, soil pH, soil organic carbon (SOC), available Zn and available water capacity (AWC) were identified as common indicators for the four ACZs. Results revealed that under the rice-wheat cropping system, ACZ-IIIB soils had a higher SHI (0.19-0.70) than other ACZs. SHI of ACZ-IIIB was significantly influenced by SOC (19.32 %), available P (10.52 %), clay (10.43 %), pH (10.80 %), and soil respiration (9.8 %). The strong relationship between SHI and system productivity of the rice-wheat (R2 = 0.79) system indicates that the selected soil health variables are representative of good soil health. It is concluded that ACZ-specific SHIs are a promising strategy for evaluating and monitoring soil health to achieve the United Nations' Sustainable Development Goal of 'zero hunger' by 2030.

Quantitative evaluation of soil health based on a minimum dataset under various short-term crop rotations on the Canadian prairies.

Maintaining and improving soil health (SH) is essential for the long-term sustainability and productivity of agriculture, notably in the face of climate change. This study addressed the challenge of selecting appropriate soil indicators, scoring methods, and indexing approaches for SH evaluation under no-till crop rotations. This study aimed to develop minimum datasets (MDS) and assess SH in six crop rotations (denoted as conventional, diversified, high-risk and high-reward, market-driven, pulse-oilseed intensified, and soil health-enhanced rotations) at three sites on the Canadian prairies. Fourteen soil indicators in the total dataset (TDS) were examined, encompassing both chemical (0-7.5 cm depth) and physical (5-10 cm depth) properties. Principal component analysis (PCA) identified MDSs from the TDS. Two scoring [linear (L) and non-linear (NL)] and two SH indexing approaches [additive (A) and weighted additive (WA)] were used to calculate the SH index (SHI). One-way ANOVA evaluated the SHI among crop rotations. The PCA revealed variations in the number of indicators in the MDS across sites, with soil organic carbon, bulk density, macroporosity, and plant-available water capacity as the common indicators for MDS across sites. Other indicators such as particulate organic matter carbon, aggregate stability, field capacity, and microporosity were found to be important, depending on the site. The non-linear weighted additive SH indexing (SHI.NLWA) proved to be the most sensitive and effective for differentiating among crop rotations in the short-term across study sites (R2 = 0.89-0.94, P < 0.05). Crop rotations significantly influenced SHI, with the diversified and high-risk and high-reward rotations having the highest SHI at Lethbridge and Scott, respectively. Overall, the diversified rotation at Lethbridge and Swift Current, along with the high-risk and high-reward rotation at Scott, exhibited better soil function than other rotations. Monitoring SHI over time and selecting crop rotations that improve SH can collectively enhance soil functions and agroecosystem productivity.

Estimation of soil health in the semi­arid regions of northwestern Iran using digital elevation model and remote sensing data.

Nowadays, neglecting soil conservation issues is one of the most critical factors in reducing soil health (SH). In this regard, to facilitate the estimation of the SH in northwestern Iran, 292 soil samples were taken from a depth of 0-30 cm of this area, and a wide range of soil properties were determined. Then, soil health indices (SHIs) were calculated. Simultaneously, the normalized difference vegetation index (NDVI), surface water capacity index (SWCI), and a digital elevation model (DEM) were obtained from satellite data. Finally, multiple linear regression (MLR) relationships between these parameters and SHIs were calculated. In this study, there was a highest significant positive correlation (P < 0.01) between IHI-LTDS and SWCI (0.71**), DEM (0.76**), and NDVI (0.73**). The MLR, with both the whole total (TDS) and minimal (MDS) dataset methods, which includes the aforementioned indices, strongly described the spatial variability of the Integrated Soil Health Index (IHI) (R2 = 0.78, AIC = - 416, RMSE = 0.05, and ρc = 0.76). According to the results of this study, it can be said that the development of SH estimation models using remote sensing extracted parameters can be one of the effective ways to reduce the cost and time of soil sampling in extensive areas.

A new method for soil health assessment based on Analytic Hierarchy Process and meta-analysis.

Understanding soil health condition is essential to the sustainability and stability of the entire ecosystem of farmland. The primary objective of this study was to improve the soil health index (SHI) based on principle component analysis (PCA) and develop a new analysis method for soil health assessment based on Meta-Analytic Hierarchy Process (Meta-AHP), which provides consistent minimum data sets (MDS), weight and scoring function for different locations, studies and management. The thirteen variables of MDS that exhibited sensitivity to management between organic and conventional soil were selected by meta-analysis. The indicator weight was assigned by a combination of experts scoring, AHP and meta-analysis. To test the applicability and sensitivity of the soil health assessment by Meta-AHP, a sixteen-year long-term test was assessed by the conventional SHI method (cSHI) and Meta-AHP. The results showed that similar evaluation results and significant positive correlations (**P < 0.01, n = 9) between the two evaluated methods were observed, and the results calculated using Meta-AHP had the best discrimination under different plant systems due to the higher F values when compared with the cSHI. This study developed a sensitive and consistent SH assessment framework that can be used applied to a variety of location, study, and soil management systems.