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Cover Picture: J. Plant Nutr. Soil Sci. 1/2026 封面图片:J. Plant nur。土壤科学1/2026
IF 2.8 3区 农林科学 Q1 AGRONOMY Pub Date : 2026-01-29 DOI: 10.1002/jpln.70050

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引用次数: 0
Issue Information: J. Plant Nutr. Soil Sci. 1/2026 期刊信息:J. Plant nur。土壤科学1/2026
IF 2.8 3区 农林科学 Q1 AGRONOMY Pub Date : 2026-01-29 DOI: 10.1002/jpln.70052
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引用次数: 0
Contents: J. Plant Nutr. Soil Sci. 1/2026 内容:J.植物营养。土壤科学1/2026
IF 2.8 3区 农林科学 Q1 AGRONOMY Pub Date : 2026-01-29 DOI: 10.1002/jpln.70051
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引用次数: 0
Issue Information: J. Plant Nutr. Soil Sci. 1/2026 期刊信息:J. Plant nur。土壤科学1/2026
IF 2.8 3区 农林科学 Q1 AGRONOMY Pub Date : 2026-01-29 DOI: 10.1002/jpln.70052
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引用次数: 0
Soil Health—What Is It Good for? 土壤健康——它有什么好处?
IF 2.8 3区 农林科学 Q1 AGRONOMY Pub Date : 2026-01-07 DOI: 10.1002/jpln.70046
Jeroen H. T. Zethof, Karsten Kalbitz, Hermann F. Jungkunst

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.

土壤健康帮助人们关注土壤科学的方式在历史上是前所未有的。然而,土壤科学家可能很难接受这个流行的术语是必要的,因为类似的术语已经存在。在这个观点中,我们想再次划定不同概念之间的界限。我们祝贺Alewell等人将这一讨论建立在坚实的基础上。愿它为生动的讨论提供基础,识别知识空白,并在坚实的科学基础上提高土壤意识。
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引用次数: 0
Optimizing Soil Properties Through Hydrogel and Sawdust Combinations for Better Plant Growth 通过水凝胶和木屑组合优化土壤特性,促进植物生长
IF 2.8 3区 农林科学 Q1 AGRONOMY Pub Date : 2025-12-23 DOI: 10.1002/jpln.70045
Kateřina Hájková, Ivana Tomášková, Tomáš Holeček, Václav Bittner, Petr Šenfeld, Adam Sikora, Karolina Resnerová, Jiří Turek, Jiří Trombik, Jan Macků

Aim

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.

Results

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.

Conclusion

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.

目的研究干旱条件下木屑与土壤混合水凝胶对杨树扦插枝吸附特性和活力的影响。结果水凝胶-木屑混合物具有较好的保水性,每克干燥混合物吸水率高达1.11 g,与单独使用土壤相比,显著提高了土壤水分。向水凝胶中添加肥料可以提高养分有效性,但不会对土壤ph产生实质性影响。值得注意的是,木屑和水凝胶的组合可以平衡养分有效性,同时保持水分水平,在干旱期间支持植物生长。生理分析表明,这些混合物对植物活力有积极影响,如提高光合性能所证明的那样。植物的化学成分基本保持稳定,纤维素和半纤维素的比例略有变化。本研究独特地将扫描电镜(SEM)分析与综合化学和生理分析相结合,突出了水凝胶在森林管理中的益处,特别是在水分胁迫条件下。
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引用次数: 0
Optimizing Soil Properties Through Hydrogel and Sawdust Combinations for Better Plant Growth 通过水凝胶和木屑组合优化土壤特性,促进植物生长
IF 2.8 3区 农林科学 Q1 AGRONOMY Pub Date : 2025-12-23 DOI: 10.1002/jpln.70045
Kateřina Hájková, Ivana Tomášková, Tomáš Holeček, Václav Bittner, Petr Šenfeld, Adam Sikora, Karolina Resnerová, Jiří Turek, Jiří Trombik, Jan Macků

Aim

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.

Results

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.

Conclusion

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.

目的研究干旱条件下木屑与土壤混合水凝胶对杨树扦插枝吸附特性和活力的影响。结果水凝胶-木屑混合物具有较好的保水性,每克干燥混合物吸水率高达1.11 g,与单独使用土壤相比,显著提高了土壤水分。向水凝胶中添加肥料可以提高养分有效性,但不会对土壤ph产生实质性影响。值得注意的是,木屑和水凝胶的组合可以平衡养分有效性,同时保持水分水平,在干旱期间支持植物生长。生理分析表明,这些混合物对植物活力有积极影响,如提高光合性能所证明的那样。植物的化学成分基本保持稳定,纤维素和半纤维素的比例略有变化。本研究独特地将扫描电镜(SEM)分析与综合化学和生理分析相结合,突出了水凝胶在森林管理中的益处,特别是在水分胁迫条件下。
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引用次数: 0
A Stochastic Approach to Quantifying the Propagation of Uncertainty in Soil Organic Carbon Content 土壤有机碳含量不确定性传播的随机量化方法
IF 2.8 3区 农林科学 Q1 AGRONOMY Pub Date : 2025-12-18 DOI: 10.1002/jpln.70038
Leonardo Inforsato, Pablo Rosso, Magdalena Main-Knorn, Ahsan Raza, Siyu Huang, Gohar Ghazaryan, Claas Nendel

Background

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.

Aim

This work introduces a methodology to analyse the error propagation from predictions of SOC digital maps, using the Monte Carlo (MC) method.

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.

Results

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.

Conclusion

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.

精准农业(PA)是一种针对特定地点的管理方法,它利用时空信息来提高生产力,同时促进可持续性。准确估计土壤性质,以及这些估计的不确定性,是必要的决策在PA。土壤有机碳(SOC)含量是准确预测作物产量所必需的土壤量。为了获得PA实施所需的大量信息,使用卫星图像已成为一种普遍做法。这使得土壤特性的空间插值成为可能。然而,这种类型的间接方法比直接测量(例如,实验室实验)具有更高的相对不确定性。虽然经常考虑由间接方法引起的土壤性质的误差评估,但却没有考虑误差的后果。本工作介绍了一种使用蒙特卡罗(MC)方法分析SOC数字地图预测误差传播的方法。方法利用原始有机碳图谱随机生成有机碳图谱的误差范围,并将这些图谱作为模拟作物产量的过程模型的输入。我们的方法评估了有机碳观测和随后的空间插值中固有的误差如何影响作物产量预测,为决策和进一步的PA实施提供了见解。结果我们的结果显示了我们所提出的方法的前景,提供了难以获得的结果。MC方法能够处理复杂的非线性误差分布,并提供全面的不确定性概率评估,这对于准确预测土壤有机碳变异对作物产量的影响具有重要意义。该方法提供了一定程度的灵活性和鲁棒性,这是确定性或更简单的分析方法无法实现的,确保了对PA的更可靠和信息更丰富的见解。
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引用次数: 0
A Stochastic Approach to Quantifying the Propagation of Uncertainty in Soil Organic Carbon Content 土壤有机碳含量不确定性传播的随机量化方法
IF 2.8 3区 农林科学 Q1 AGRONOMY Pub Date : 2025-12-18 DOI: 10.1002/jpln.70038
Leonardo Inforsato, Pablo Rosso, Magdalena Main-Knorn, Ahsan Raza, Siyu Huang, Gohar Ghazaryan, Claas Nendel

Background

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.

Aim

This work introduces a methodology to analyse the error propagation from predictions of SOC digital maps, using the Monte Carlo (MC) method.

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.

Results

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.

Conclusion

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.

精准农业(PA)是一种针对特定地点的管理方法,它利用时空信息来提高生产力,同时促进可持续性。准确估计土壤性质,以及这些估计的不确定性,是必要的决策在PA。土壤有机碳(SOC)含量是准确预测作物产量所必需的土壤量。为了获得PA实施所需的大量信息,使用卫星图像已成为一种普遍做法。这使得土壤特性的空间插值成为可能。然而,这种类型的间接方法比直接测量(例如,实验室实验)具有更高的相对不确定性。虽然经常考虑由间接方法引起的土壤性质的误差评估,但却没有考虑误差的后果。本工作介绍了一种使用蒙特卡罗(MC)方法分析SOC数字地图预测误差传播的方法。方法利用原始有机碳图谱随机生成有机碳图谱的误差范围,并将这些图谱作为模拟作物产量的过程模型的输入。我们的方法评估了有机碳观测和随后的空间插值中固有的误差如何影响作物产量预测,为决策和进一步的PA实施提供了见解。结果我们的结果显示了我们所提出的方法的前景,提供了难以获得的结果。MC方法能够处理复杂的非线性误差分布,并提供全面的不确定性概率评估,这对于准确预测土壤有机碳变异对作物产量的影响具有重要意义。该方法提供了一定程度的灵活性和鲁棒性,这是确定性或更简单的分析方法无法实现的,确保了对PA的更可靠和信息更丰富的见解。
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引用次数: 0
Climate-Smart Tillage and Residue Return Enhance Carbon Sequestration and Yield in Cereal–Legume Intercropping Systems 气候智能型耕作和秸秆还田提高了谷物-豆类间作系统的碳固存和产量
IF 2.8 3区 农林科学 Q1 AGRONOMY Pub Date : 2025-12-12 DOI: 10.1002/jpln.70043
Sana ur Rehman, Qihong Tu, Hossam S. El-Beltagi, Maqshoof Ahmad, Azhar Hussain, Haishui Yang, Atta ur Rehman Khan, Mahmoud Ahmed Amer, Muhammad Taimoor Shakeel, Muhammad Ishaq, Muhammad Irfan, Rabia Manzoor, Muhammad Jawad Babar, Rashid Iqbal, Xiaoping Xin

Background

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.

Aim

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.

Method

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.

Results

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.

Conclusion

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.

背景:面对日益加剧的气候变化,可持续的农业做法对于增强土壤健康和作物生产力至关重要。虽然保护性耕作和作物残茬管理可以提高资源利用效率和碳固存,但它们对谷物-豆科作物间作系统土壤碳动态和产量的综合影响,特别是在半干旱地区,仍未得到充分探讨。目的研究免耕配残茬还田对玉米-大豆间作系统土壤有机碳固存和作物生产力的协同效应。方法采用常规耕作(CT)、少耕(MT)、RT和留茬管理(留茬还田和不留茬还田)等不同耕作方式,在玉米-大豆间作中进行了2年的田间试验。结果在单大豆栽培中,RT联合残茬还田显著提高了土壤养分有效性,硝酸盐(8.65 mg kg−1)、磷(6.7 mg kg−1)和钾(85 mg kg−1)的浓度最高,优于CT。RT还提高了总有机碳(TOC)含量(6.1 g kg−1)和碳固存率(2.42 Mg ha−1年−1),而单独玉米的CT处理值较低(5.3 g kg−1和2.00 Mg ha−1年−1),强调了豆类系统的碳储存潜力。在生产力方面,轮作和轮作均提高了粮食产量和土地当量比(LER分别为1.43和1.38),而轮作表现最差(LER为1.08-1.09)。然而,这些结果是特定于半干旱条件的,其更广泛的适用性可能取决于土壤类型、管理历史和不同地区的环境变异性等因素。结论在半干旱条件下,将气候智能型耕作与残茬返还相结合是提高谷物-豆科作物系统环境可持续性和农业生产力的有效策略。
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引用次数: 0
期刊
Journal of Plant Nutrition and Soil Science
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