Soil Quality Assessment Research Articles

Soil Quality Variation under Different Land Use Types and Its Driving Factors in Beijing

With the advancement of urbanization, land resources are becoming increasingly strained, particularly for urban greening purposes. In this context, a large number of newly cultivated lands dominated by construction waste and backfill soil are emerging in cities. Assessing the soil quality of these newly cultivated lands and achieving their rational utilization accurately and quantitatively has become an urgent issue. In this study, soil samples of five land use types, namely newly cultivated land (NCL, control), adjacent cropland (CL), arbor–shrub mixed forest (ASF), arbor forest (AF), and shrubland (SL) were selected around Beijing, China. ASF, AF, and SL are also newly cultivated lands composed of construction waste and backfill before greening. Based on principal component analysis (PCA), a total data set (TDS) and a minimum data set (MDS) were used to construct the soil quality index (SQI) model. Soil quality indicators covering the physical and chemical characteristics of the soil and their relationships with land use types were studied with the Partial Least Squares Path Model (PLS-PM). The results were summarized as follows: (1) The soil quality index under different land use types in the Beijing plain area were in the order of arbor–shrub mixed forest (ASF) > arbor forest (AF) > shrubland (SL) > cropland (CL) > newly cultivated land (NCL). (2) Soil organic carbon (SOC), soil water content (SWC), maximum water-holding capacity (MWHC), capillary water-holding capacity (CWHC), Pb, and Cd were identified as the MDS. The MDS of the soil quality assessment model showed a linear relationship with the TDS (y = 0.946x + 0.050, R2 = 0.51). (3) Land use types have an indirect impact on soil quality by changing the content of Pb. The chemical indicators’ coefficient (0.602) contributed more to the SQI than did the physical indicators’ (0.259) and heavy metal elements’ (−0.234). In general, afforestation and agricultural production could improve the newly cultivated lands’ soil quality, but afforestation is much better than agricultural production. These results will help to evaluate the SQI in the Beijing plain area objectively and accurately, and they have significant implications for soil restoration and management.

Exploring farmer’s assessment of soil quality and root yield in cassava-based cropping systems

Farmers’ knowledge of soil quality and yield assessment were evaluated among cassava farmers in southwestern Nigeria. Data were collected on farmers’ demography, farming experience, criteria for selecting a site for cassava cultivation and preferences for those criteria, farmers’ methods of yield prediction and how it compares with the scientific approach, farmers’ agronomic knowledge and how it relates to the realities of climate change and soil fertility decline. The modal age class of the farmers is 45–55 years, and most of the farmers are male. The results show that farmers use a combination of soil and vegetation-based criteria to assess soil quality from which the decision to cultivate a given land is made. Among the soil-based criteria, soil drainage, colour, and depth rank the most and most used. Most of the farmers assessed yield before harvesting through green healthy leaves (25 %), large and shiny stems (25 %) and soil cracks (50 %). The predicted and measured cassava yields on farmer-managed farms were comparable, with an R2 of 0.63, although farmers overestimated the cassava root yield. There is a unanimous consensus among farmers that yield has declined in the last decade, with a mean of 36 %. The main cause of yield decline was attributed to declining rainfall and poor soils. Our study concluded that farmers have a good understanding of soil health and cassava agronomy through a process of trial and error and ingenuity as farmers’ local indices were consistent with conventional soil health indicators. It was suggested that, by recognising and incorporating traditional methods used by farmers to assess soil quality, we could enhance soil management strategies and raise productivity at the farm level.

Effect of Biochar Application on the Improvement of Soil Properties and Buckwheat (Fagopyrum esculentum Moench) Yield on Two Contrasting Soil Types in a Semi-Arid Region of Inner Mongolia

Biochar application to soil is widely recognized as a promising agricultural management practice to increase crop production by enhancing the physical, chemical, and microbiological properties of the soil. Despite the fact that numerous studies have investigated biochar production and alterations in soil properties, the effects of biochar on contrasting soils within the same region remain poorly understood, especially for semi-arid regions. Therefore, a three-year field experiment was initiated in 2020 wherein biochar was applied once to a buckwheat field at rates of 0, 20, 40, and 60 Mg ha−1 (BC0: no biochar; BC1: 20 Mg ha−1; BC2: 40 Mg ha−1; BC3: 60 Mg ha−1) for two soil types (aeolian sandy and grey meadow soil) in the northeast of Inner Mongolia, China. The soil water storage (SWS), nutrient contents (organic matter, available nitrogen, phosphorus, and potassium), microbial biomass (carbon, nitrogen, and phosphorus), and enzyme activities (urease, invertase, and alkaline phosphatase) were assessed at a soil depth of 0–15 cm as part of the soil quality assessment, and the buckwheat grain yield was estimated for crop productivity evaluation. The results showed that biochar amendment improved selected soil physicochemical and microbiological properties and buckwheat yields for both soil types. Compared to BC0, the biochar addition increased buckwheat yields, on average, by 11.23% to 22.82% in aeolian sandy soil and by 7.36% to 14.87% in grey meadow soil across three years. The results of principal component analysis (PCA) and random forest analysis (RFA) indicate that soil available nutrients and microbiological properties were the most important factors influencing buckwheat yields in aeolian sandy soil and grey meadow soil, respectively. Based on RFA, the available potassium, phosphorus, and nitrogen were found to contribute at rates of 13.10%, 10.06%, and 8.12%, respectively, to buckwheat yields in aeolian sandy soil. In contrast, alkaline phosphatase, urease, and microbial biomass carbon contribute 20.26%, 8.48%, and 7.82%, respectively, to the buckwheat yields in grey meadow soil. Following biochar addition, there was greater improvement in soil health and buckwheat production for aeolian sandy soil than grey meadow soil. In conclusion, biochar addition is an effective practice for improving soil health and crop productivity in both aeolian sandy soil and grey meadow soil in semi-arid regions.

Soil Quality Indices Based on Biological Properties to Assess Chemical Soil Degradation by Metal(loid)s

ABSTRACT Soil quality assessments, rarely applied to contaminated soils with metal(loid)s, consider ecological topics such as multifunctionality, resistance and resilience of microbial communities to perturbations, in addition to ecosystem services. In this study, the effect of metal(loid) contamination was evaluated in soils based on physicochemical and biological properties at different distances from an industrial complex and a Cu smelter. From these properties, soil quality indices (SQIs) were tested to compare their sensitivity and relationship with metal(loid) contamination. Enzyme activities and the average well color development, obtained from the community-level physiological profile, were the most suitable soil quality indicators. These variables along with some physicochemical properties [e.g. total organic carbon, pH, and available metal(loid) contents] were selected to develop SQIs. Five SQIs were calculated, from which the Multifunctionality assessment and the AreaSQI were found to be suitable methods to develop SQIs due to their higher discriminant power and relationship with metal(loid) contamination compared to other SQIs. They were validated using data from the literature, and both showed decreasing values at higher levels of metal(loid) contamination. Although the AreaSQI represents an attractive approach due to its visualization and easy interpretation, the multifunctionality approach showed a stronger sensitivity to chemical degradation by metal(loid)s in soil and offered an easier and faster calculation alternative.

Assessing Soil Quality, Wheat Crop Yield, and Water Productivity under Condition of Deficit Irrigation.

Wheat is one of the most important cereal crops in Egypt and all over the world. Its productivity is adversely affected by drought due to deficient irrigation to provide nutrients required for plant growth. In a field experiment, silicon foliar applications at concentrations of 0, 200, and 400 mg L-1 were performed at different irrigation rates ranging from 1000 to 4000 m3 ha-1 to assess water irrigation productivity and wheat crop yield in a calcareous soil under arid climate conditions. Increased irrigation rates led to a significant increase in soil nutrient dynamics, as well as in the number and weight of grains per spike, leaf area index, grain yield, straw yield, and biological yield, with the exception of the weight of 1000 grains. Spraying with sodium silicate had a significant impact on grain yield and harvest index but did not significantly impact the other traits. Furthermore, the interaction between irrigation and silicate application rates showed significance only for grain yield, the number of spikes/m2, and the harvest index. Applying three times irrigation could produce the highest nutrient retention, wheat yield, and water irrigation productivity. No significance was observed between 3000 m3 ha-1 and 4000 m3 ha-1 irrigation, indicating a saving of 25% of applied irrigation water. It can be concluded that applying irrigation at 3000 m3 ha-1 could be a supplemental irrigation strategy. High wheat grain yield can be achieved under deficit irrigation (3000 m3 ha-1) on the northwestern coast of Egypt with an arid climate by spraying crops with sodium silicate at a rate of 400 mg L-1.

Assessment of Soil Spatial Variability in Agricultural Ecosystems Using Multivariate Analysis, Soil Quality Index (SQI), and Geostatistical Approach: A Case Study of the Mnasra Region, Gharb Plain, Morocco

Accurate assessment of soil quality is crucial for sustainable agriculture and soil conservation. Thus, this study aimed to assess soil quality in the agricultural ecosystem of the Mnasra region within the Gharb Plain of Morocco, employing a comprehensive approach integrating multivariate analysis and geostatistical techniques. Thirty soil samples were collected from the surface layers across thirty selected sites. The results showed significant variations in soil properties across the study area, influenced by factors such as soil texture, parent material, and agricultural practices. Pearson correlation and principal component analysis (PCA) were employed to analyze the relationships among soil properties and compute the Soil Quality Index (SQI). The SQI revealed values ranging from 0.48 to 0.74, with 46.66% of sampled soils classified as “Good” and 53.33% as “Fair”. Geostatistical analysis, particularly ordinary kriging (OK) interpolation and semivariogram modeling, highlighted the spatial variability of soil properties, aiding in mapping soil quality across the landscape. The integrated approach demonstrates the importance of combining field assessments, statistical analyses, and geospatial techniques for comprehensive soil quality evaluation and informed land management decisions. These findings offer valuable insights for decision-makers in monitoring and managing agricultural land to promote sustainable development in the Gharb region of Morocco.

Assessment of Soil Health and Soil Quality of Bokaro, District, Jharkhand, India

Taking care of soil health is most importance for sustainable crop production. Soil quality is a blending of soil processes and provides to a measure change in soil condition as related to following factors such as land use, climate patterns, cropping sequences and farming systems. In the present investigation analysis of different physical and chemical characteristics were made to determine soil health of Bokaro district. Results show that soil bulk density was in normal range, soil pore space % varied from 39.85 to 48.32 g cm-3, particle density ranged from 2.24 to 2.69 Mg m-3, water holding capacity varied from 34.23 to 43.86 %. Soil pH and EC were found to be acidic and non-saline in nature respectively while soil organic carbon and available phosphorus were in medium to high range. Available nitrogen and potassium were found to be low to medium range i.e., 197.0 to 219.0 Kg ha - 1 and 201.96 to 266.01 Kg ha -1 and the range of phosphorus varied from 16.96 to 26.68 kg ha -1 respectively. Deficiency of sulphur followed by zinc was found in soils of Bokaro district.

Long-Term Straw Incorporation under Controlled Irrigation Improves Soil Quality of Paddy Field and Rice Yield in Northeast China.

Soil quality is an indicator of the ability to ensure ecological security and sustainable soil usage. The effects of long-term straw incorporation and different irrigation regimes on the yield and soil quality of paddy fields in cold regions remain unclear. This study established four treatments: controlled irrigation + continuous straw incorporation for 3 years (C3), controlled irrigation + continuous straw incorporation for 7 years (C7), flooded irrigation + continuous straw incorporation for 3 years (F3), and flooded irrigation + continuous straw incorporation for 7 years (F7). Analysis was conducted on the impact of various irrigation regimes and straw incorporation years on the physicochemical characteristics and quality of the soil. The soil quality index (SQI) for rice fields was computed using separate datasets for each treatment. The soil nitrate nitrogen, available phosphorus, soil organic carbon, and soil organic matter contents of the C7 were 93.51%, 5.80%, 8.90%, and 8.26% higher compared to C3, respectively. In addition, the yield of the C7 treatment was 5.18%, 4.89%, and 10.32% higher than those of F3, C3, and F7, respectively. The validity of the minimum data set (MDS) was verified by correlation, Ef and ER, which indicated that the MDS of all treatments were able to provide a valid evaluation of soil quality. The MDS based SQI of C7 was 11.05%, 11.97%, and 27.71% higher than that of F3, C3, and F7, respectively. Overall, long-term straw incorporation combined with controlled irrigation increases yield and soil quality in paddy fields in cold regions. This study provides a thorough assessment of soil quality concerning irrigation regimes and straw incorporation years to preserve food security and the sustainability of agricultural output. Additionally, it offers a basis for soil quality diagnosis of paddy fields in the Northeast China.

Soil Quality Assessment Under Different Vegetation Restoration Strategies in the Karst Rocky Deserted Area of Southwestern China

Soil Quality Assessment Under Different Vegetation Restoration Strategies in the Karst Rocky Deserted Area of Southwestern China

Biochar boosted high oleic peanut production with enhanced root development and biological N fixation by diazotrophs in a sand-loamy Primisol

Peanut yield and quality face significant threats due to climate change and soil degradation. The potential of biochar technology to address this challenge remains unanswered, though biochar is acknowledged for its capacity to enhance the soil microbial community and plant nitrogen (N) supply. A field study was conducted in 2021 on oil peanuts grown in a sand-loamy Primisol that received organic amendments at 20 Mg ha−1. The treatments consisted of biochar amendments derived from poultry manure (PB), rice husk (RB), and maize residue (MB), as well as manure compost (OM) amendment, compared to no organic amendment (CK). In 2022, during the second year after amendment, samples of bulk topsoil, rooted soil, and plants were collected at the peanut harvest. The analysis included the assessment of soil quality, peanut growth traits, microbial community, nifH gene abundance, and biological N fixation (BNF) rate. Compared to the CK, the OM treatment led to an 8 % increase in peanut kernel yield, but had no effect on kernel quality in terms of oil production. Conversely, both PB and MB treatments increased kernel yield by 10 %, whereas RB treatment showed no change in yield. Moreover, all biochar amendments significantly improved oilseed quality by 10–25 %, notably increasing the proportion of oleic acid by up to 70 %. Similarly, while OM amendment slightly decreased root development, all biochar treatments significantly enhanced root development by over 80 %. Furthermore, nodule number, fresh weight per plant, and the nifH gene abundance in rooted soil remained unchanged under OM and PB treatments but was significantly enhanced under RB and MB treatments compared to CK. Notably, all biochar amendments, excluding OM, increased the BNF rate and N-acetyl-glucosaminidase activity. These changes were attributed to alterations in soil aggregation, moisture retention, and phosphorus availability, which were influenced by the diverse physical and chemical properties of biochars. Overall, maize residue biochar contributed synergistically to enhancing soil fertility, peanut yield, and quality while also promoting increased root development, a shift in the diazotrophic community and BNF.

Building biological indicators for soil quality assessment in Dong Thap Muoi (Mekong Delta Vietnam): An application of the expert Delphi

Assessing soil quality promotes sustainable agricultural practices, especially in areas where agriculture is vital to the economy. This paper focuses on building biological indicators to assess soil quality and to promote sustainable agricultural practices in the Dong Thap Muoi region of the Mekong Delta in Vietnam. A panel of 17 experts with extensive experience in soil biology and agricultural sciences is gathered to help identify these indicators and to create a reliable framework using the Expert Delphi Method, a consensus-building approach. After a test round and two official rounds of the Delphi process, three main dimensions (biological organisms, biological quality, and relational indicators) and a total of 24 specific indicators are identified. The research finding highlights the importance of biological indicators in evaluating soil health and how they can help improve legal frameworks. These indicators are key for making decisions about land management, policy development, resource distribution, and environmental protection in selected area. Additionally, the need for further research to develop experimental models and practical solutions is indicated, ultimately advancing sustainable agricultural practices particularly in Dong Thap Muoi and more broadly in the Mekong Delta, Vietnam.

Worldwide Examination of Magnetic Responses to Heavy Metal Pollution in Agricultural Soils

Over the last decade, a large number of studies have been conducted on heavy metals and magnetic susceptibility (χlf) measurement in soils. Yet, a global understanding of soil contamination and magnetic responses remains elusive due to the limited scope or sampling sites of these studies. Hence, we attempted to explore a pollution proxy on a global scale. Through a meta-analysis of data from 102 published studies, our research aimed to provide a worldwide overview of heavy metal pollution and magnetic responses in agriculture soils. We mapped the geographic distribution of nine heavy metals (Cr, Cu, Zn, Pb, Ni, As, Cd, Mn, and Fe) in agricultural soils and explored their pollution sources and contributions. Since 2011, The accumulation of heavy metals has escalated, with industrial activities (31.5%) being the largest contributor, followed by agricultural inputs (27.1%), atmospheric deposition (22.66%), and natural sources (18.74%). The study reports χlf ranging from 6.45 × 10−8 m3/kg to 319.23 × 10−8 m3/kg and χfd from 0.59% and 12.85%, with the majority of the samples being below 6%, indicating heavy metal influence mainly from human activities. Pearson’s correlation and redundancy analysis show significant positive correlations of Pb, Zn, and Cu with χlf (r = 0.51–0.53) and Mn and Fe with χfd (r = 0.50–0.53), while Pb, Zn, Cu, and As metals were shown to be key factors of variation in magnetic response. The average heavy metal pollution load index of 2.03 suggests moderate global agricultural soil pollution, with higher heavy metal contamination in areas of high χlf. Regression analysis confirms soil is considered to be non-polluted below χlf of 26×10−8 m3/kg and polluted above this threshold, with all contamination factors of metals showing a linear correlation with χlf (R = 0.72), indicating that a significant relationship between χlf and the geochemical properties of soils continues to exist on a global scale. This study provides new insights for large-scale agricultural soil quality assessment and magnetic response.

Soil quality assessment of karst trough valley under different bedrock strata dip and land-use types, based on a minimum data set

Typical bedrock strata dips and land use types can significantly influence the soil quality in karst trough valleys. However, data on the influence of bedrock strata dips on the assessment of soil quality based on the soil quality index in karst trough valleys are limited. Thus, the aim of this study was to investigate the impact of different land use types and bedrock strata dips on soil quality and to evaluate the suitability of soil quality assessment methods for karst trough valleys. Five land-use types were selected as research objects for each dip and anti-dip slope, and a total data set (TDS) consisting of soil physico-chemical properties and soil microbial property indices was collected. The minimum data set (MDS) was constructed using principal component analysis (PCA) and norm values, and two evaluation methods, i.e., linear (SQI-L) and non-linear (SQI-NL) were used to assess the soil quality in the study area. The results showed that the order of weight of the MDS was urease activity > protease activity > silt > soil bulk density > total phosphorus. The overall soil quality obtained from the SQI-L and SQI-NL models exhibited similar results for the dip slope; however, there were differences in the details of the anti-dip slope. The soil quality of the anti-dip slope was better than that of the dip slope (P < 0.05) and the soil quality indices of the forest and grassland were significantly higher than those of the other land use types (P < 0.05). Furthermore, the SQI-NL model can accurately replace the SQI-L model for soil quality evaluation based on its high variation interval and variable coefficients. Overall, our results suggest that the SQI-NL model is suitable for soil quality evaluation on dip and anti-dip slopes, and returning farmland to forest is an effective land-use change in the karst trough valleys.

Development of Regenerative Tea Cultivation Models through Dual Approach of Soil and Plant Health Management towards Crop Sustainability, Soil Quality Development, Pesticide Reduction and Climate Change Mitigation: A Case Study from Lakhipara Tea Estate,

Climate change is a reality and its impact on the tea plantations is apparent in the rise of pest intensity, higher agrochemical use, inconsistent crop yields, declining grand growth period, increased abiotic stress and various other challenges; that threaten long-term sustainability of the Indian tea sector. The urgency to adopt sustainable practices is increasing by the day but to gain time bound results a comprehensive focus encompassing soil and plant health development will be crucial. To deal with climate challenge head-on, the Indian tea industry is making changes at various levels of operations. The sustainable tea initiative at Lakhipara tea estate, was one such attempt by the Goodricke Group Limited in their Dooars tea growing region of West Bengal. The program was initiated in 2014 with an aim to reduce pesticide use, improve soil quality, and produce quality teas while sustaining crop yields, improving renewable energy use and lowering the carbon footprint. Adoption of Inhana Rational Farming (IRF) Technology and taking the essence from ‘Trophobiosis Theory’ of French Scientist F. Chaboussou, the program focused on management of soil and plant health. Three years evaluation of crop yields in respect of the budgeted crop, revealed on an average an excess of 78 kg/ha/year in the project area. In rest of the garden area a contrasting crop loss of 118 kg was recorded per ha during the same period. The higher crop performance positively correlated with the higher nutrient use efficiency which was 17.7 percent higher in the project area (NUENPK : 8.86) as compared to the general garden area (NUENPK : 7.53), which reflected the impact of plant health management towards enhanced nutrient uptake, assimilation and utilization. Assessment of pesticide usage, revealed up to 77% decrease in usage in the project area during 2014-16, as compared to the pre- project year. Comparison with pesticide usage of the Dooars tea growing region during this same period, indicated a 62% lower Crop Pesticide Pollution Index (CPPI) in the project area. The finding indicated a 52 to 77% reduction in the accumulated toxicity potential of the applied pesticides in the project area; thereby accrediting safer tea development under this program, when adjudged in terms of pesticide residue. Assessment of soil quality revealed an overall eight percent increment in Soil Fertility Index (FI) value, with significant improvement in soil microbial activity potential (MAP) values i.e., by almost four times. The finding pointed towards the favourable impact of soil health management primarily through Novcom composting towards enhancement of soil microbial interactions. Post three years of experimentation the overall Soil Quality Iindex (SQI) value increased by 6% in the project area. The finding corroborated a concurrent 6.72% increase in the soil organic carbon stock during the same period. Reduction in use of non- renewable inputs viz. chemical fertilizers and pesticides in the project area was indicated by approximately 40 percent enhancement in energy use efficiency and energy productivity post the assessment period. Carbon assessment in terms of kg CO2 equivalents/ kg made tea (using ACFA version 1.0) indicated approx. 65 to 70 % lower footprint in the project area, primarily due to 20 to 30 % reduction in chemical fertilizers and 60 to 70 % reduction in the use of synthetic pesticides. The results indicated that while integrated soil management is the pre-requisite criteria towards rejuvenation of soil health and for restoration of the habitat for predators, it does not play a direct role in reducing the pest pressure and simultaneously the requirement of pesticides. Physiologically activated plants/ bushes on the other hand; due to their higher nutrient assimilation capacity and efficient protein synthesis are always lesser susceptible to pest attack. Hence, focus on activation of plant physiology can reduce the plant- pest interaction leading to a natural reduction in the requirement for pesticides vis-à-vis the pesticide usage.

The Western Greece Soil Information System (WΕSIS)—A Soil Health Design Supported by the Internet of Things, Soil Databases, and Artificial Intelligence Technologies in Western Greece

Soil quality is vital for ecosystem stability, impacting human, plant, and animal health. Traditional soil quality assessments are labor-intensive and costly, making them unsuitable for smart agriculture. To overcome this, Internet of Things (IoT) and artificial intelligence (AI) technologies are employed for sustainable agriculture, enabling real-time data collection and analysis, trend identification, and soil health optimization. The Western Greece Soil Information System (WΕSIS) offers open-access data and services for soil health and sustainability. It includes modules for soil quality indicators, sustainable fertilization management zones, soil property distribution, prediction, mapping, statistical analysis, water management, land use maps, digital soil mapping, and crop health calculation. Integrating the IoT and AI allows for real-time and remote monitoring of soil conditions, managing soil interventions adaptively and in a data-driven way, enhancing soil resources’ efficiency and sustainability, and increasing crop yield and quality. AI algorithms assist farmers and regional stakeholders in optimizing production lines, methodologies, and field practices, reducing costs and increasing profitability. This promotes a circular economy, a soil- and climate-resilient future, biodiversity protection targets, and enhanced soil fertility and productivity. The proposed IoT/AI technical architecture can underpin the development of soil health monitoring platforms, integrating data from various sources, automating data collection, and providing decision support tools.

Improving soil organic carbon mapping in farmlands using machine learning models and complex cropping system information

Obtaining accurate spatial maps of soil organic carbon (SOC) in farmlands is crucial for assessing soil quality and achieving precision agriculture. The cropping system is an important factor that affects the soil carbon cycle in farmlands, and different agricultural managements under different cropping systems lead to spatial heterogeneity of SOC. However, current research often ignores differences in the main controlling factors of SOC under different cropping systems, especially when the cropping pattern is complex, which is not conducive to farmland zoning management. This study aims to (i) obtain the spatial distribution map of six cropping systems by using multi-phase HJ-CCD satellite images; (ii) explore the stratified heterogeneous relationship between SOC and environmental variables under different cropping systems by using the Cubist model; and (iii) predict the spatial map of SOC. The Xiantao, Tianmen, and Qianjiang cities, which are the core agricultural areas of the Jianghan Plain, were selected as the study area. Results showed that the SOC content in rice–wheat rotation was the highest among the six cropping systems. The Cubist model outperformed random forest, ordinary kriging, and multiple linear regression in SOC mapping. The results of the Cubist model showed that cropping system, climate, soil attributes, and vegetation index were important influencing factors of SOC in farmlands. The main controlling factors of SOC under different cropping systems were different. Specifically, summer crop types had a greater influence on spatial variations in SOC than winter crops. Paddy–upland rotation was more affected by river distance and NDVI, while upland–upland rotation was more affected by irrigation-related factors. This work highlights the differentiated main controlling factors of SOC under different cropping systems and provides data support for farmland zoning management. The Cubist model can improve the prediction accuracy of SOC under complex cropping systems.

An Accurate Approach for Predicting Soil Quality Based on Machine Learning in Drylands

Nowadays, machine learning (ML) is a useful technology due to its high accuracy in constructing non-linear models and algorithms that can adapt to the complexity and diversity of data. Thus, the current work aimed to predict the soil quality index (SQI) from extensive soil data, achieving high accuracy with the artificial neural networks (ANN) model. However, the efficiency of ANN depends on the accuracy of the data that is prepared for training. For this purpose, MATLAB programming language was used to enable the calculation, classification, and compilation of the results into databases within a few minutes. The proposed MATLAB program was highly efficient, accurate, and quick in calculating soil big data for training the machine compared with traditional methods. The database contains 306 vector sets, 80% of them are used for training and the remaining 20% are reserved for testing. The optimal model obtained comprises one hidden layer with 250 neurons and one output layer with a sigmoid function. The ANN achieved a high coefficient of determination (R2) values for SQI estimation, with around 0.97 and 0.98 for training and testing, respectively. The results indicate that 36.93% of the total soil samples belonged to the very high quality class (C1). In contrast, the high quality (C2), moderate quality (C3), low quality (C4), and very low quality (C5) classes accounted for 10.46%, 31.37%, 20.92%, and 0.33% of the samples, respectively. The high contents of CaCO3, pH, sodium saturation, salinity, and clay content were identified as limiting factors in certain areas. The results of this study indicated high accuracy of soil quality assessment using physical, chemical, and fertility soil features in regression analysis with ANN. This method, which is suitable for arid zones, enhances agricultural productivity and decision-making by identifying critical soil quality categories and constraints.

Assessment of Land Quality for Siamese Orange (Citrus nobilis var. microcarpa) Development in Pacitan Regency, Indonesia

Siamese oranges are one of the most widely developed horticultural crops in Indonesia. Arjosari and Ngadirojo are some of the sub-districts that produce Siamese orange in Pacitan Regency. One of the factors that can affect the development of citrus is land quality. This study aims to obtain land quality index data and improvement efforts for developing Siamese orange. Soil samples were taken from 25 land mapping units (LMUs) with 87 sample points. The soil sampling points were determined based on area or sampling density. Data analysis used for soil quality assessment includes correlation, principal component analysis (PCA), minimum data set (MDS), and calculation of climate quality index. The parameters used in this study are bulk density, porosity, moisture content, texture, pH, electrical conductivity (EC), cation exchange capacity (CEC), base saturation, total N, available P, available K, soil organic carbon (SOC), microbial biomass carbon (MBC), evapotranspiration, temperature, and solar radiation. Based on the results of soil quality analysis in the study area, it has a value range of 0.28 to 0.37, including low class, while the climate quality index is 1.39 with moderate class. The land quality index has a very low to low class with a value of 0.39 to 0.51. Intrinsic, extrinsic, and anthropogenic factors can influence soil quality. The study area’s limiting factors for developing Siamese oranges are EC, total N, and available P, which can be improved by making furrow ponds, adding organic fertilizer, using urea fertilizer, and SP36 according to the dosage.

Nanotechnology-based Sensors for Real-time Monitoring and Assessment of Soil Health and Quality: A Review

Soil health and quality are critical factors in maintaining sustainable agriculture, ecosystem stability, and global food security. Conventional methods for assessing soil properties are often time-consuming, labor-intensive, and lack real-time monitoring capabilities. Nanotechnology has emerged as a promising approach to develop advanced sensors for rapid, in-situ, and continuous monitoring of soil health parameters. This comprehensive review discusses the recent advancements in nanotechnology-based sensors for soil health assessment, their working principles, applications, challenges, and future prospects. We highlight the potential of various nanomaterials, such as carbon nanotubes, graphene, metal oxide nanoparticles, and quantum dots, in fabricating highly sensitive, selective, and robust soil sensors. The integration of these nanosensors with wireless communication technologies and data analytics enables real-time monitoring and precision agriculture practices. Furthermore, we discuss the environmental and ecological implications of deploying nanosensors in soil and the need for standardized protocols and regulations. This review provides valuable insights into the current state-of-the-art and future directions of nanotechnology-based sensors for soil health monitoring, promoting sustainable agriculture and environmental management.

A framework for selecting and assessing soil quality indicators for sustainable soil management in waste dumps

The primary objective of this study was to develop soil quality indexes (SQIs) to reveal the changes in SQ during the restoration of vegetation in the reclaimed waste dumps of the Hequ open-pit coal mine. The study built an SQI evaluation model for waste dumps based on the soil management assessment framework. The total data set (TDS) consisted of nine physicochemical property indicators. The selection of the minimum data set (MDS) involved the utilization of principal component analysis (PCA) and Norm values. The SQ was comprehensively evaluated for nine indicators, taking into account the non-linear membership function and the improved Nemerow index. The findings suggested a notable disparity in the SQ between the reclaimed area and the unreclaimed area, yet the overall SQ fell short. In the TDS index system, the organic matter has the highest weight and a greater contribution to the soil quality of the waste dumps. In the MDS indicator system, the weights of organic matter and total nitrogen are both 0.5. According to Nemerow index method, the average SQIN of 5 plots is calculated to be 0.4352 ± 0.194. The average value obtained from TDS is 0.581 ± 0.236, and the average value obtained from MDS is 0.602 ± 0.351. The weighted additive method was employed to compute three SQIs, all of which yielded satisfactory outcomes. And the above evaluation methods indicate that the overall soil quality level of the waste dumps is at a moderate level. The sequence of SQ in various waste dumps was as follows: No.4lower > No.1 > No.2 > No.3 > No.4upper. Specifically, the non-linear membership function indicated that pH, available nitrogen (AN), available phosphorus (AP), surface moisture content (SMC), and bulk density (BD) were crucial in limiting SQIs in total waste dumps. The crucial limiting SQIs in unreclaimed areas were total phosphorus (TP) and total nitrogen (TN). This analysis demonstrates its efficacy in formulating strategies for the SQ evaluation and targeted soil reclamation plans of waste dumps.