Probability of cultivating Se-rich maize in Se-poor farmland based on intensive field sampling and artificial neural network modelling

Using machine learning to predict selenium content in crops: Implications for soil health and agricultural land utilization in longevity regions

Selenium (Se) is an indispensable trace element to human health, yet its biological tolerance threshold is relatively narrow. The potential application of machine learning methods to indirectly predict the Se content in crops across regional areas, thereby validating the reasonableness of soil health thresholds, remains to be explored. This study analyzed the factors influencing Se absorption in crops from longevity regions and employed machine learning models to predict the bioconcentration factor of Se, thereby obtaining selenium content in these crops and ultimately estimated the Se threshold for healthy soils. The results indicated that the Artificial Neural Network (ANN) model demonstrated the best predictive performance for the bioaccumulation factor (BAF) of Se in crops. The maximum permissible concentration of Se in rice was 0.17 mg/kg, while the minimum was 0.03 mg/kg; for maize, the maximum permissible concentration was 0.25 mg/kg, and the minimum was 0.04 mg/kg. Approximately 68 % of the arable land in the study area was suitable for cultivating Se-rich crops, providing important insights for the optimization of crop cultivation.

Driving factors of molybdenum (Mo) bioconcentration in maize in the Longitudinal Range-Gorge Region of Southwestern China

Molybdenum (Mo) plays an important role in maintaining plant growth and human health. Assessment studies on the driving factors of Mo migration in soil-crop systems are crucial for ensuring optimal agricultural and human health. The Mo bioconcentration factor (BCF-Mo) is a useful tool for evaluating Mo bioavailability in soil-crop systems. However, the influence pathways and degrees of different environmental factors on BCF-Mo remain poorly understood. In this context, 109 rhizosphere and maize grain samples were collected from the Longitudinal Range-Gorge Region (LRGR) in Linshui County, Sichuan Province, China, and analyzed for the contents of Mo and other soil physiochemical parameters to explore the spatial patterns of BCF-Mo and its driving factors. Areas with the highest BCF-Mo values were mainly observed in the southern and northern parts of the Huaying and Tongluo mountains. The influence degrees of the selected environmental factors in this study followed the order of normalized difference vegetation index (NDVI) < elevation (EL) < mean annual humidity (MAH) < slope (SL) < mean annual temperature (MAT). The MAH and NDVI directly influenced the BCF-Mo values. The EL and MAT mainly indirectly affected the BCF-Mo values by influencing the rhizosphere organic matter (OM) contents, while the SL mainly affected the BCF-Mo values by influencing the rhizosphere pH. Therefore, OM and pH of the rhizosphere were the main influencing factors of BCF-Mo in the study area. In summary, the selected environmental factors mainly exhibited indirect influences on BCF-Mo by directly affecting the physicochemical properties of the rhizosphere.

Uptake of zinc from the soil to the wheat grain: Nonlinear process prediction based on artificial neural network and geochemical data

Trace elements in plants primarily derive from soils, subsequently influencing human health through the food chain. Therefore, it is essential to understand the relationship of trace elements between plants and soils. Since trace elements from soils absorbed by plants is a nonlinear process, traditional multiple linear regression (MLR) models failed to provide accurate predictions. Zinc (Zn) was chosen as the objective element in this case. Using soil geochemical data, artificial neural networks (ANN) were utilized to develop predictive models that accurately estimated Zn content within wheat grains. A total of 4036 topsoil samples and 73 paired rhizosphere soil-wheat samples were collected for the simulation study. Through Pearson correlation analysis, the total content of elements (TCEs) of Fe, Mn, Zn, and P, as well as the available content of elements (ACEs) of B, Mo, N, and Fe, were significantly correlated with the Zn bioaccumulation factor (BAF). Upon comparison, ANN models outperformed MLR models in terms of prediction accuracy. Notably, the predictive performance using ACEs as input factors was better than that using TCEs. To improve the accuracy, a two-step model was established through multiple testing. Firstly, ACEs in the soil were predicted using TCEs and properties of the rhizosphere soil as input factors. Secondly, the Zn BAF in grains was predicted using ACE as input factors. Consequently, the content of Zn in wheat grains corresponding to 4036 topsoil samples was predicted. Results showed that 85.69 % of the land was suitable for cultivating Zn-rich wheat. This finding offers a more accurate method to predict the uptake of trace elements from soils to grains, which helps to warn about abnormal levels in grains and prevent potential health risks.

Transcriptomics, proteomics, and metabolomics interventions prompt crop improvement against metal(loid) toxicity

The escalating challenges posed by metal(loid) toxicity in agricultural ecosystems, exacerbated by rapid climate change and anthropogenic pressures, demand urgent attention. Soil contamination is a critical issue because it significantly impacts crop productivity. The widespread threat of metal(loid) toxicity can jeopardize global food security due to contaminated food supplies and pose environmental risks, contributing to soil and water pollution and thus impacting the whole ecosystem. In this context, plants have evolved complex mechanisms to combat metal(loid) stress. Amid the array of innovative approaches, omics, notably transcriptomics, proteomics, and metabolomics, have emerged as transformative tools, shedding light on the genes, proteins, and key metabolites involved in metal(loid) stress responses and tolerance mechanisms. These identified candidates hold promise for developing high-yielding crops with desirable agronomic traits. Computational biology tools like bioinformatics, biological databases, and analytical pipelines support these omics approaches by harnessing diverse information and facilitating the mapping of genotype-to-phenotype relationships under stress conditions. This review explores: (1) the multifaceted strategies that plants use to adapt to metal(loid) toxicity in their environment; (2) the latest findings in metal(loid)-mediated transcriptomics, proteomics, and metabolomics studies across various plant species; (3) the integration of omics data with artificial intelligence and high-throughput phenotyping; (4) the latest bioinformatics databases, tools and pipelines for single and/or multi-omics data integration; (5) the latest insights into stress adaptations and tolerance mechanisms for future outlooks; and (6) the capacity of omics advances for creating sustainable and resilient crop plants that can thrive in metal(loid)-contaminated environments.

Using machine learning to predict selenium and cadmium contents in rice grains from black shale-distributed farmland area

Cadmium (Cd) and selenium (Se) are widely enriched in soil at black shale outcropping areas, with Cd levels exceeding the standard (2.0 mg/kg in 5.5 < pH ≤ 6.5) commonly. The prevention of Cd hazards and the safe development of Se-rich land resources are key issues that need to be urgently addressed. To ensure safe utilization of Se-rich land in the CdSe coexisting areas, 158 rice samples, their corresponding rhizosphere soils, and 8069 topsoil samples were collected and tested in the paddy fields of Ankang City, Shaanxi Province, where black shales are widely exposed. The results showed that 43 % of the topsoil samples were Se-rich soil (Se > 0.4 mg/kg) wherein 79 % and 3 % of Cd concentrations exceeded the screening value and control value, respectively, according to the GB15618-2018 standard. Meanwhile, 63 % of the rice samples were Se rich (Se > 0.04 mg/kg) and the Cd content exceeded the prescribed limit (0.2 mg/kg) in Se-rich rice by 26 %. There was no significant positive correlation between the Se and Cd contents in the rice grains and the Se and Cd contents in the corresponding rhizosphere soil. The factors influencing Se and Cd uptake in rice were SiO2, CaO, P, S, pH, and TFe2O3. Accordingly, an artificial neural network (ANN) and multiple linear regression model (MLR) were used to predict Cd and Se bioaccumulation in rice grains. The stability and accuracy of the ANN model were better than those of the MLR model. Based on survey data and the prediction results of the ANN model, a safe planting zoning of Se-rich rice was proposed, which provided a reference for the scientific planning of land resources.

Identification of factors driving the spatial distribution of molybdenum (Mo) in topsoil in the Longitudinal Range-Gorge Region of Southwestern China using the Geodetector model

As a key component of plant nitrogen-fixing enzymes and a variety of human coenzyme factors, molybdenum (Mo) plays an essential role in supporting both plant growth and human health. Soil is a key medium for the cycling of Mo in the biosphere. However, the driving anthropogenic and natural factors governing the spatial distribution of Mo in soil and their interactions are not well understood. To determine the factors that affect the spatial patterns of Mo in topsoil, 6980 samples were collected from the Longitudinal Range-Gorge Region (Linshui County, Sichuan Province, China). In this area, tall mountains are adjacent to deep valleys. Topsoil with enriched Mo is mostly distributed in mountainous areas. The most important factors influencing Mo in topsoil are soil parent materials (q = 0.482), altitude (q = 0.256), and soil type (q = 0.259). There are synergistic effects among the various driving factors [q(X1 ∩ X2) > Max[q(X1), q(X2)]]. The Geodetector model was used to validate the magnitude of the interaction effects. The contribution to interacting factors is nonlinearly enhanced when the contribution of a single factor was low (any two factors of aspect, road distance, land use type, and S). The contribution to interacting factors is enhanced bidirectionally when the contribution of a single factor was high (any two factors of altitude, soil type, soil parent material, OM, and TFe2O3). When the contribution of one factor is high and the other is low, the contributing to interacting factors is mostly enhanced bidirectionally and a few are nonlinearly enhanced.

Modeling the feasibility of Se-rich corn cultivation in Se-deficient agricultural fields using random forest algorithm

Selenium constitutes an essential trace element for the human body. Moderate Se intake plays a pivotal role in preserving overall health. The absorption of Se by plants is primarily influenced by the available Se levels in soils, rather than by the soil total Se content, offering potential for exploring Se-rich crops in Se-deficient regions. In this study, we explore the factors influencing the Se bioaccumulation coefficient in corn based on a land quality geochemical survey at a 1:50,000 scale and establish predictive models for corn seed Se content using random forest and multiple linear regression approaches. The results indicate that the surface soil in the study area is deficient in Se (0.18-1.21 mg/kg), but 54% of the corn grain samples met the standards for Se-rich products (0.02-0.30 mg/kg). The factors influencing the Se biological enrichment coefficient in corn seeds are soil pH and CaO and MgO content, with impact levels of 0.54, 0.42, and 0.35, respectively. Compared to multiple linear regression models, the RF model provides more accurate and reliable predictions of corn Se content. The random forest model indicates that approximately 41% of the farmland within the study area is conducive to the cultivation of naturally Se-rich corn, which is a 26% increase in the planting area compared to recommendations based solely on soil Se content. In this research, we introduce an innovative methodological framework for organically cultivating naturally Se-rich corn within regions affected by Se deficiency.

Influencing factors of selenium transformation in a soil-rice system and prediction of selenium content in rice seeds: a case study in Ninghua County, Fujian Province

Selenium (Se) is an essential element for human and animal health and has antioxidant, anticancer, and antiviral effects. However, more than 100 million people in China do not have enough Se in their diets, resulting in a state of low Se in the human body. Since the absorption of Se by crop seeds depends not only on the Se content in soil, there are many omissions and misjudgments in the division of Se-rich producing areas. Soil pH, total iron oxide content (TFe2O3), soil organic matter (SOM), and P and S contents were the main factors affecting Se migration and transformation in the soil-rice system. In this study, we compared the performance of the back propagation neural network (BP network) and multiple linear regression (MLR) using 177 pairs of soil-rice samples. Our results showed that the BP network had higher accuracy than MLR. The accuracy and precision of the prediction data met the requirements, and the prediction data were reliable. Based on the Se data of surface paddy fields, 26,900 ha of Se-rich rice planting area was planned using this model, accounting for 77% of the paddy field area. In the planned Se-rich area for rice, the proportion of soil Se content greater than 0.4 mg·kg-1 was only 5.29%. Our research is of great significance for the development of Se-rich lands.

Prediction of cadmium contents in rice grains from Quaternary sediment-distributed farmland using field investigations and machine learning

The Quaternary sediment-distributed regions of South China are suitable for rice cultivation, which is crucial for ensuring food security. Spatial correlations between soil cadmium (Cd) and rice Cd contents are generally poor, making the evaluation of rice quality and associated health risks challenging. In this study, we developed machine learning algorithms for predicting rice Cd contents using 654 data pairs of soil-rice samples collected in Guangxi province, China. After a comprehensive comparison, our results showed that the random forest (RF) had the better performance than artificial neural network (ANN) based on all the data (RMSEtesting 0.066 vs. 0.099 and R2testing 0.860 vs. 0.688). The feature importance analysis showed that soil CaO, Cd, elevation, and rainfall were the four most important features affecting the rice Cd contents in the study area. Using the established RF-predicated model, the rice Cd contents were predicted at the provincial level with an additional dataset of 1176 paddy soil samples. The prediction result revealed about 23 % of farmland cultivated rice with Cd content over 0.2 mg kg-1 in the study area. Therefore, it is recommended to implement strict measures by local agricultural departments to reduce rice Cd contents and ensure food safety in these areas. Our study provides valuable insights into the prediction of rice Cd contents, thus contributing to ensuring food safety and preventing Cd exposure-associated health risks.