The way in which soil health is helping to draw people's attention to soil science is unprecedented in history. Yet, soil scientists may find it difficult to accept that this popularized term is necessary, as similar terms already exist. In this opinion, we want to draw once more the boundaries between the different concepts. We congratulate Alewell et al. for placing this discussion on a solid ground. May it provide a basis for vivid discussion, identify knowledge gaps, and boost soil awareness on a solid scientific foundation.
This multidisciplinary study explores the effects of hydrogel mixed with sawdust and soil on the sorption properties and vitality of poplar cuttings under drought conditions.
The hydrogel–sawdust mixture exhibited superior water retention, absorbing up to 1.11 g water per gram of dry mixture, significantly improving soil moisture compared to soil alone. Fertilizer addition to the hydrogel enhanced nutrient availability without substantially affecting soil pH. Notably, the combination of sawdust and hydrogel balances nutrient availability while maintaining moisture levels, supporting plant growth during drought. Physiological analyses indicate that these mixtures positively affect plant vitality, as evidenced by increased photosynthetic performance. Chemical composition of plants remained largely stable, with minor changes in the proportion of cellulose and hemicelluloses.
This study uniquely combines scanning electron microscopy (SEM) analysis, and comprehensive chemical and physiological analyses to highlight the benefits of hydrogel in forest management, particularly under water stress conditions.
This multidisciplinary study explores the effects of hydrogel mixed with sawdust and soil on the sorption properties and vitality of poplar cuttings under drought conditions.
The hydrogel–sawdust mixture exhibited superior water retention, absorbing up to 1.11 g water per gram of dry mixture, significantly improving soil moisture compared to soil alone. Fertilizer addition to the hydrogel enhanced nutrient availability without substantially affecting soil pH. Notably, the combination of sawdust and hydrogel balances nutrient availability while maintaining moisture levels, supporting plant growth during drought. Physiological analyses indicate that these mixtures positively affect plant vitality, as evidenced by increased photosynthetic performance. Chemical composition of plants remained largely stable, with minor changes in the proportion of cellulose and hemicelluloses.
This study uniquely combines scanning electron microscopy (SEM) analysis, and comprehensive chemical and physiological analyses to highlight the benefits of hydrogel in forest management, particularly under water stress conditions.
Precision agriculture (PA) is a site-specific management approach that utilises spatiotemporal information to improve productivity while also promoting sustainability. Accurate estimates of soil properties, along with the uncertainty of these estimates, are necessary for decision-making in PA. An essential soil quantity required to accurately predict crop yield is the soil organic carbon (SOC) content. To obtain the large amount of information necessary for PA implementation, the use of satellite images has become a common practice. This allows the spatial interpolation of soil properties. However, this type of indirect approach carries higher relative uncertainties than direct measurements (e.g., laboratory experiments). Although error evaluations of soil properties resulting from indirect approaches are constantly considered, the consequences of error are not.
This work introduces a methodology to analyse the error propagation from predictions of SOC digital maps, using the Monte Carlo (MC) method.
We stochastically generated an error range for SOC maps, using one original map of SOC, and used these maps as inputs for a process-based model that simulated crop yields. Our approach evaluates how the error inherent in SOC observations and the subsequent spatial interpolation impacts crop yield forecasting, providing insights for decision-making and further PA implementation.
Our results show promise in the proposed method, delivering results that are difficult to obtain. The MC method was able to handle complex, non-linear error distributions and provide a comprehensive probabilistic assessment of uncertainty, which is important for accurately predicting the impact of SOC variability on crop yield.
This method offers a degree of flexibility and robustness that is not achievable with deterministic or simpler analytical approaches, ensuring more reliable and informative insights for PA.
Precision agriculture (PA) is a site-specific management approach that utilises spatiotemporal information to improve productivity while also promoting sustainability. Accurate estimates of soil properties, along with the uncertainty of these estimates, are necessary for decision-making in PA. An essential soil quantity required to accurately predict crop yield is the soil organic carbon (SOC) content. To obtain the large amount of information necessary for PA implementation, the use of satellite images has become a common practice. This allows the spatial interpolation of soil properties. However, this type of indirect approach carries higher relative uncertainties than direct measurements (e.g., laboratory experiments). Although error evaluations of soil properties resulting from indirect approaches are constantly considered, the consequences of error are not.
This work introduces a methodology to analyse the error propagation from predictions of SOC digital maps, using the Monte Carlo (MC) method.
We stochastically generated an error range for SOC maps, using one original map of SOC, and used these maps as inputs for a process-based model that simulated crop yields. Our approach evaluates how the error inherent in SOC observations and the subsequent spatial interpolation impacts crop yield forecasting, providing insights for decision-making and further PA implementation.
Our results show promise in the proposed method, delivering results that are difficult to obtain. The MC method was able to handle complex, non-linear error distributions and provide a comprehensive probabilistic assessment of uncertainty, which is important for accurately predicting the impact of SOC variability on crop yield.
This method offers a degree of flexibility and robustness that is not achievable with deterministic or simpler analytical approaches, ensuring more reliable and informative insights for PA.
Sustainable agricultural practices are crucial for enhancing soil health and crop productivity in the face of increasing climate variability. Although conservation tillage and crop residue management are known to enhance resource use efficiency and carbon sequestration, their combined effects on soil carbon dynamics and yield in cereal–legume intercropping systems, especially in semiarid regions, remain underexplored.
This study examines the synergistic effect of combining reduced tillage (RT) paired with residue return on soil organic carbon sequestration and crop productivity in a maize–soybean intercropping system.
A field experiment was conducted using different tillage practices, such as conventional tillage (CT), minimum tillage (MT), RT, and residue management approaches (with and without residue return) in a maize–soybean intercropping setup over 2 years.
RT combined with residue return in sole soybean cultivation significantly enhanced soil nutrient availability, recording the highest concentrations of nitrate (8.65 mg kg−1), phosphorus (6.7 mg kg−1), and potassium (85 mg kg−1), outperforming CT. RT also improved total organic carbon (TOC) content (6.1 g kg−1) and carbon sequestration rate (2.42 Mg ha−1 year−1), compared to lower values under CT with sole maize (5.3 g kg−1 and 2.00 Mg ha−1 year−1), emphasizing the carbon storage potential of legume-based systems. In terms of productivity, both CT and RT improved grain yield and land equivalent ratio (LER: 1.43 and 1.38, respectively), whereas MT consistently showed the lowest performance (LER: 1.08–1.09). However, the outcomes are site-specific to semiarid conditions, and their broader applicability may depend on factors such as soil type, management history, and environmental variability across different regions.
These findings demonstrate that combining climate-smart tillage with residue return is an effective strategy to enhance both environmental sustainability and agricultural productivity in cereal–legume systems under semiarid conditions.
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