Climate Smart Agriculture最新文献

Global warming enhances soybean suitability and yield potential in the mid-high latitude Amur River Basin 全球变暖增强了大豆在中高纬度阿穆尔河流域的适宜性和产量潜力

Climate Smart Agriculture

Pub Date : 2026-02-01 DOI: 10.1016/j.csag.2026.100099

Xinyue Chang , Lingxue Yu , Zhuoran Yan , Lun Bao , Xuan Li

Understanding the spatiotemporal dynamics of soybean yield potential under climate change is crucial for improving crop productivity and guiding adaptive agricultural strategies in climatically sensitive regions. However, systematic assessments that integrate both historical and future climate conditions with crop suitability remain limited. In this study, we employed the Agro-Ecological Zones model to quantify soybean yield potential and climatic suitability across the Amur River Basin from 1980 to 2100, integrating historical observations and CMIP6 climate projections. Results indicate a continuous increase in yield potential under both rain-fed and irrigated conditions. Under SSP2-4.5 and SSP5-8.5, rain-fed soybean yields rise by 0.05 and 0.10 t ha decade⁻¹, while irrigated soybean yields increase by 0.04 and 0.12 t ha⁻¹ decade⁻¹. China and Russia show consistent productivity gains, especially in the Russian Far East under high-emission scenarios, whereas yield improvements in Mongolia depend strongly on irrigation due to persistent water limitations. Soybean suitability exhibits pronounced northwestward expansion driven by enhanced thermal and hydrological conditions. Moderately suitable areas expand by 135.97–185.97 % under rain-fed conditions and 106.51–132.10 % under irrigation. The suitability centroid migrates northwestward by up to 320.5 km by 2100, remaining within Heilongjiang Province. These findings highlight significant opportunities for soybean cultivation in northern high-latitude zones under climate warming. To harness this potential and strengthen system resilience, we recommend: (i) suitability-informed land reallocation, (ii) targeted investment in precision irrigation, and (iii) adaptive crop management aligned with shifting agro-climatic conditions, alongside enhanced transboundary cooperation among China, Russia, and Mongolia.

了解气候变化条件下大豆产量潜力的时空动态变化，对气候敏感地区提高作物生产力和指导适应性农业战略具有重要意义。然而，将历史和未来气候条件与作物适宜性结合起来的系统评估仍然有限。本研究采用农业生态区模型，结合历史观测和CMIP6气候预估，量化了1980 - 2100年阿穆尔河流域大豆产量潜力和气候适宜性。结果表明，在旱作和灌溉条件下，产量潜力都在不断增加。在SSP2-4.5和SSP5-8.5条件下，旱作大豆产量每10年增加0.05和0.10吨，而灌溉大豆产量每10年增加0.04和0.12吨。中国和俄罗斯表现出持续的生产力增长，特别是在高排放情景下的俄罗斯远东地区，而蒙古由于持续的水资源限制，产量的提高在很大程度上取决于灌溉。由于热、水文条件的增强，大豆适宜性表现出明显的西北扩张。中等适宜面积在旱作条件下扩大135.97 ~ 185.97%，在灌溉条件下扩大106.51 ~ 132.10%。到2100年，适宜性质心向西北移动320.5 km，仍在黑龙江省境内。这些发现突出了气候变暖下北方高纬度地区大豆种植的重大机遇。为了利用这一潜力并加强系统抵御能力，我们建议：(1)基于适宜性的土地再分配；(2)有针对性的精准灌溉投资；(3)与不断变化的农业气候条件相一致的适应性作物管理；同时加强中国、俄罗斯和蒙古之间的跨境合作。

{"title":"Global warming enhances soybean suitability and yield potential in the mid-high latitude Amur River Basin","authors":"Xinyue Chang , Lingxue Yu , Zhuoran Yan , Lun Bao , Xuan Li","doi":"10.1016/j.csag.2026.100099","DOIUrl":"10.1016/j.csag.2026.100099","url":null,"abstract":"<div><div>Understanding the spatiotemporal dynamics of soybean yield potential under climate change is crucial for improving crop productivity and guiding adaptive agricultural strategies in climatically sensitive regions. However, systematic assessments that integrate both historical and future climate conditions with crop suitability remain limited. In this study, we employed the Agro-Ecological Zones model to quantify soybean yield potential and climatic suitability across the Amur River Basin from 1980 to 2100, integrating historical observations and CMIP6 climate projections. Results indicate a continuous increase in yield potential under both rain-fed and irrigated conditions. Under SSP2-4.5 and SSP5-8.5, rain-fed soybean yields rise by 0.05 and 0.10 t ha decade<sup>−1</sup>, while irrigated soybean yields increase by 0.04 and 0.12 t ha<sup>−1</sup> decade<sup>−1</sup>. China and Russia show consistent productivity gains, especially in the Russian Far East under high-emission scenarios, whereas yield improvements in Mongolia depend strongly on irrigation due to persistent water limitations. Soybean suitability exhibits pronounced northwestward expansion driven by enhanced thermal and hydrological conditions. Moderately suitable areas expand by 135.97–185.97 % under rain-fed conditions and 106.51–132.10 % under irrigation. The suitability centroid migrates northwestward by up to 320.5 km by 2100, remaining within Heilongjiang Province. These findings highlight significant opportunities for soybean cultivation in northern high-latitude zones under climate warming. To harness this potential and strengthen system resilience, we recommend: (i) suitability-informed land reallocation, (ii) targeted investment in precision irrigation, and (iii) adaptive crop management aligned with shifting agro-climatic conditions, alongside enhanced transboundary cooperation among China, Russia, and Mongolia.</div></div>","PeriodicalId":100262,"journal":{"name":"Climate Smart Agriculture","volume":"3 1","pages":"Article 100099"},"PeriodicalIF":0.0,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146090649","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Estimation of soybean phenotypic parameters across growth stages using UAV-based multi-source feature fusion and XGBoost 基于无人机多源特征融合和XGBoost的大豆各生育期表型参数估计

Climate Smart Agriculture

Pub Date : 2026-01-18 DOI: 10.1016/j.csag.2026.100098

Zhimin Liu , Dawei Ding , Abdoul Kader Mounkaila Hamani , Weiguang Zhai , Jia Tian , Yuhong Wang , Lexuan Zhang , Guangshuai Wang , Yadan Du

While essential for precision agriculture, the accurate and dynamic monitoring of crop phenotypic parameters faces challenges, including the constraints of single-data sources and insufficient model generalization across growth stages. This research introduced an integrated framework that leverages multi-source data fusion and the XGBoost algorithm to estimate key soybean parameters, including Leaf Area Index (LAI) and Above-Ground Biomass (AGB). Field experiments incorporated different irrigation methods (drip/micro-sprinkler) and planting densities (210,000/270,000 plants ha⁻¹), multispectral images and corresponding ground truth data were acquired across five critical growth stages.We extracted 11 vegetation indices (V) and 8 texture features (T) and constructed inversion models using Support Vector Regression (SVR), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost) based on single and multi-source (V + T) features. The results indicated that: the multi-source feature fusion model outperformed single-feature models. The XGBoost algorithm outperformed all other models, achieving average R² values of 0.673, and 0.671, and RMSE values of 0.117, and 79.751 kg ha⁻¹ for LAI, and AGB inversion, respectively. The full pod stage (R4) was identified as the optimal remote sensing observation window, where the best models achieved R² values of 0.846 (LAI) and 0.731 (AGB), with RMSE values of 0.131 and 81.01 kg ha⁻¹, respectively. Drip irrigation combined with high planting density significantly (P < 0.05) increased soybean LAI and AGB. This study provides a robust, high-throughput technical solution for dynamic crop phenotyping, it highlights the value of fusing multi-source UAV features with machine learning for advancing data-driven smart agriculture.

作物表型参数的准确和动态监测对精准农业至关重要，但也面临着挑战，包括单一数据源的限制和不同生长阶段模型泛化不足。本研究引入了一个集成框架，利用多源数据融合和XGBoost算法来估计大豆的关键参数，包括叶面积指数（LAI）和地上生物量（AGB）。田间试验采用不同的灌溉方式（滴灌/微喷）和种植密度（21万/27万株/公顷），获得了五个关键生长阶段的多光谱图像和相应的地面真实数据。我们提取了11个植被指数(V)和8个纹理特征(T)，并利用支持向量回归（SVR）、随机森林（RF）和基于单源和多源（V + T）特征的极端梯度增强（XGBoost）技术构建了反演模型。结果表明：多源特征融合模型优于单特征模型。XGBoost算法优于所有其他模型，LAI和AGB反演的平均R2值分别为0.673和0.671，RMSE值分别为0.117和79.751 kg ha−1。确定全荚果期（R4）为最佳遥感观测窗口，最佳模型的R2值分别为0.846 （LAI）和0.731 (AGB)， RMSE值分别为0.131和81.01 kg ha−1。滴灌配合高种植密度显著提高了大豆LAI和AGB （P < 0.05）。这项研究为动态作物表型分析提供了一个强大的、高通量的技术解决方案，它突出了将多源无人机特征与机器学习融合在一起，以推进数据驱动的智能农业的价值。

{"title":"Estimation of soybean phenotypic parameters across growth stages using UAV-based multi-source feature fusion and XGBoost","authors":"Zhimin Liu , Dawei Ding , Abdoul Kader Mounkaila Hamani , Weiguang Zhai , Jia Tian , Yuhong Wang , Lexuan Zhang , Guangshuai Wang , Yadan Du","doi":"10.1016/j.csag.2026.100098","DOIUrl":"10.1016/j.csag.2026.100098","url":null,"abstract":"<div><div>While essential for precision agriculture, the accurate and dynamic monitoring of crop phenotypic parameters faces challenges, including the constraints of single-data sources and insufficient model generalization across growth stages. This research introduced an integrated framework that leverages multi-source data fusion and the XGBoost algorithm to estimate key soybean parameters, including Leaf Area Index (LAI) and Above-Ground Biomass (AGB). Field experiments incorporated different irrigation methods (drip/micro-sprinkler) and planting densities (210,000/270,000 plants ha<sup>−1</sup>), multispectral images and corresponding ground truth data were acquired across five critical growth stages.We extracted 11 vegetation indices (V) and 8 texture features (T) and constructed inversion models using Support Vector Regression (SVR), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost) based on single and multi-source (V + T) features. The results indicated that: the multi-source feature fusion model outperformed single-feature models. The XGBoost algorithm outperformed all other models, achieving average R<sup>2</sup> values of 0.673, and 0.671, and RMSE values of 0.117, and 79.751 kg ha<sup>−1</sup> for LAI, and AGB inversion, respectively. The full pod stage (R4) was identified as the optimal remote sensing observation window, where the best models achieved R<sup>2</sup> values of 0.846 (LAI) and 0.731 (AGB), with RMSE values of 0.131 and 81.01 kg ha<sup>−1</sup>, respectively. Drip irrigation combined with high planting density significantly (<em>P</em> < 0.05) increased soybean LAI and AGB. This study provides a robust, high-throughput technical solution for dynamic crop phenotyping, it highlights the value of fusing multi-source UAV features with machine learning for advancing data-driven smart agriculture.</div></div>","PeriodicalId":100262,"journal":{"name":"Climate Smart Agriculture","volume":"3 1","pages":"Article 100098"},"PeriodicalIF":0.0,"publicationDate":"2026-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146037759","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Mitigation and adaptation strategies in climate-smart agriculture: A review for sustainable production 气候智能型农业中的减缓和适应战略：可持续生产综述

Climate Smart Agriculture

Pub Date : 2026-01-09 DOI: 10.1016/j.csag.2026.100097

Muhammad Awais , Xiuquan Wang , Muhammad Umair Ashraf

Climate change affects global farming, particularly smallholder farmers who are already struggling with challenges such as poor yields, water shortages, and limited access to technology. However, agriculture is the source of approximately 25–30 % of global greenhouse gas (GHG) emissions, making sustainable agriculture solutions essential. To address this, climate-smart agriculture (CSA) is often promoted as a means of achieving sustainable development. This review paper used PRISMA methodology to examine the potential of CSA to contribute to both emission reduction (mitigation) and farming systems resilience (adaptation) by analyzing 368 peer-reviewed articles published between 2012 and 2025. Results stated that four CSA pathways, soil carbon sequestration, precision fertilization, methane-reducing livestock feed, and agroforestry, were found to consistently achieve emission reductions of 20–40 %, soil carbon increments of 0.3–0.8 t C ha⁻¹ yr⁻¹, and productivity improvements of 10–25 %. On the adaptation side, climate-resilience crop varieties, smart irrigation, cover cropping, and mixed farming systems emerged as scalable solutions that simultaneously enhance productivity and ecosystem stability. Regional analysis revealed that developed countries (e.g., the United States, Germany) emphasize technology-driven precision agriculture and carbon management, while developing regions (e.g., Kenya, Ethiopia, India) focus on agroforestry, rainwater harvesting, and low-input resilience practices. The results provide excellent guidance for researchers, policy makers, and development agencies focused on developing climate-resilience food systems.

气候变化影响到全球农业，特别是小农，他们已经在努力应对诸如产量低、水资源短缺和获得技术的机会有限等挑战。然而，农业占全球温室气体（GHG）排放量的25%至30%，因此可持续农业解决方案至关重要。为了解决这一问题，气候智慧型农业（CSA）往往被作为实现可持续发展的一种手段加以推广。本综述通过分析2012年至2025年间发表的368篇经同行评审的文章，使用PRISMA方法考察了农业生态系统对减排（缓解）和农业系统复原力（适应）的潜力。结果表明，土壤固碳、精准施肥、减少甲烷的牲畜饲料和农林业这四种CSA途径可以持续实现20 - 40%的碳减排，土壤碳增量为0.3-0.8 t C / ha−1年−1，生产力提高10 - 25%。在适应方面，气候适应型作物品种、智能灌溉、覆盖种植和混合农业系统成为可扩展的解决方案，同时提高了生产力和生态系统的稳定性。区域分析显示，发达国家（如美国、德国）强调技术驱动的精准农业和碳管理，而发展中国家（如肯尼亚、埃塞俄比亚、印度）则侧重农林业、雨水收集和低投入弹性实践。研究结果为致力于发展气候适应型粮食系统的研究人员、政策制定者和发展机构提供了极好的指导。

{"title":"Mitigation and adaptation strategies in climate-smart agriculture: A review for sustainable production","authors":"Muhammad Awais , Xiuquan Wang , Muhammad Umair Ashraf","doi":"10.1016/j.csag.2026.100097","DOIUrl":"10.1016/j.csag.2026.100097","url":null,"abstract":"<div><div>Climate change affects global farming, particularly smallholder farmers who are already struggling with challenges such as poor yields, water shortages, and limited access to technology. However, agriculture is the source of approximately 25–30 % of global greenhouse gas (GHG) emissions, making sustainable agriculture solutions essential. To address this, climate-smart agriculture (CSA) is often promoted as a means of achieving sustainable development. This review paper used PRISMA methodology to examine the potential of CSA to contribute to both emission reduction (mitigation) and farming systems resilience (adaptation) by analyzing 368 peer-reviewed articles published between 2012 and 2025. Results stated that four CSA pathways, soil carbon sequestration, precision fertilization, methane-reducing livestock feed, and agroforestry, were found to consistently achieve emission reductions of 20–40 %, soil carbon increments of 0.3–0.8 t C ha<sup>−1</sup> yr<sup>−1</sup>, and productivity improvements of 10–25 %. On the adaptation side, climate-resilience crop varieties, smart irrigation, cover cropping, and mixed farming systems emerged as scalable solutions that simultaneously enhance productivity and ecosystem stability. Regional analysis revealed that developed countries (e.g., the United States, Germany) emphasize technology-driven precision agriculture and carbon management, while developing regions (e.g., Kenya, Ethiopia, India) focus on agroforestry, rainwater harvesting, and low-input resilience practices. The results provide excellent guidance for researchers, policy makers, and development agencies focused on developing climate-resilience food systems.</div></div>","PeriodicalId":100262,"journal":{"name":"Climate Smart Agriculture","volume":"3 1","pages":"Article 100097"},"PeriodicalIF":0.0,"publicationDate":"2026-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146037758","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Climate-aware hybrid 1D-CNN-LSTM model for multi-layer soil moisture prediction in tropical Cocoa plantations 气候感知混合1D-CNN-LSTM模型用于热带可可种植园多层土壤湿度预测

Climate Smart Agriculture

Pub Date : 2025-12-28 DOI: 10.1016/j.csag.2025.100096

Sarowar Morshed Shawon , Mukter Zaman , Shamala Maniam , Tee Yei Kheng , H.Y. Wong

Accurate multi-layer soil moisture (SM) prediction is crucial for optimizing irrigation management and supporting sustainable Cocoa production, yet modelling SM dynamics in tropical environments remains challenging due to strong climatic variability, heterogeneous soils, and depth-specific interactions. To address these challenges, this study develops a climate-aware hybrid convolutional neural network, long short-term memory (CNN-LSTM) model capable of predicting SM across five root-zone layers (M1–M5) in Cocoa (Theobroma cacao) plantations in Malaysia. The model integrates multi-source environmental variables including temperature, humidity, rainfall, solar radiation, wind speed, wind gust, and dew point with in-situ multi-depth soil measurements to capture the complex spatial and temporal dynamics that characterize humid tropical agroecosystems. While the CNN component extracts localized spatial patterns, the LSTM component effectively learns long-term temporal dependencies, enabling accurate depth-specific SM forecasting. Model performance for moisture prediction was assessed using standard regression metrics (MSE, RMSE, MAE, MAPE, and R²), with results showing consistently high accuracy across all soil layers and zones (R² > 0.94; average RMSE <0.9). Time-series and scatter plot analyses further confirmed strong agreement between observed and predicted values. Importantly, unlike earlier research, this study not only predicts multi-layer root-zone soil moisture but also demonstrates reliable layer-wise forecasting up to 3 days ahead in a tropical, high-humidity Cocoa farm, providing a novel contribution to the existing literature. By offering robust, depth-resolved soil moisture predictions and short-term forecasts, this hybrid deep learning framework establishes a practical foundation for automated, climate-aware irrigation systems in perennial tropical crops. The results highlight how combining environmental feature engineering with advanced deep learning can strengthen data-driven decision support and enhance the resilience and sustainability of Cocoa production under Malaysia's humid, rainfall-variable climate.

准确的多层土壤水分（SM）预测对于优化灌溉管理和支持可可可持续生产至关重要，但由于强烈的气候变异性、异质土壤和深度特异性相互作用，在热带环境中建立SM动态模型仍然具有挑战性。为了解决这些挑战，本研究开发了一种气候感知混合卷积神经网络，长短期记忆（CNN-LSTM）模型，能够预测马来西亚可可（Theobroma可可）种植园五个根区层（M1-M5）的SM。该模型将温度、湿度、降雨量、太阳辐射、风速、阵风和露点等多源环境变量与现场多深度土壤测量相结合，以捕捉潮湿热带农业生态系统复杂的时空动态特征。当CNN组件提取局部空间模式时，LSTM组件有效地学习长期时间依赖性，从而实现准确的深度特定SM预测。使用标准回归指标（MSE、RMSE、MAE、MAPE和R2）评估模型的湿度预测性能，结果显示所有土层和区域都具有一致的高精度（R2 > 0.94；平均RMSE <；0.9）。时间序列和散点图分析进一步证实了观测值和预测值之间的强烈一致性。重要的是，与早期的研究不同，这项研究不仅预测了多层根区土壤湿度，而且还在热带高湿度可可农场中展示了可靠的提前3天分层预测，为现有文献提供了新的贡献。通过提供强大的、深度分辨的土壤湿度预测和短期预测，这种混合深度学习框架为多年生热带作物的自动化、气候感知灌溉系统奠定了实践基础。研究结果强调了如何将环境特征工程与先进的深度学习相结合，以加强数据驱动的决策支持，并提高马来西亚潮湿、降雨量多变的气候下可可生产的弹性和可持续性。

{"title":"Climate-aware hybrid 1D-CNN-LSTM model for multi-layer soil moisture prediction in tropical Cocoa plantations","authors":"Sarowar Morshed Shawon , Mukter Zaman , Shamala Maniam , Tee Yei Kheng , H.Y. Wong","doi":"10.1016/j.csag.2025.100096","DOIUrl":"10.1016/j.csag.2025.100096","url":null,"abstract":"<div><div>Accurate multi-layer soil moisture (SM) prediction is crucial for optimizing irrigation management and supporting sustainable Cocoa production, yet modelling SM dynamics in tropical environments remains challenging due to strong climatic variability, heterogeneous soils, and depth-specific interactions. To address these challenges, this study develops a climate-aware hybrid convolutional neural network, long short-term memory (CNN-LSTM) model capable of predicting SM across five root-zone layers (M1–M5) in Cocoa (<em>Theobroma cacao</em>) plantations in Malaysia. The model integrates multi-source environmental variables including temperature, humidity, rainfall, solar radiation, wind speed, wind gust, and dew point with in-situ multi-depth soil measurements to capture the complex spatial and temporal dynamics that characterize humid tropical agroecosystems. While the CNN component extracts localized spatial patterns, the LSTM component effectively learns long-term temporal dependencies, enabling accurate depth-specific SM forecasting. Model performance for moisture prediction was assessed using standard regression metrics (MSE, RMSE, MAE, MAPE, and R<sup>2</sup>), with results showing consistently high accuracy across all soil layers and zones (R<sup>2</sup> > 0.94; average RMSE <0.9). Time-series and scatter plot analyses further confirmed strong agreement between observed and predicted values. Importantly, unlike earlier research, this study not only predicts multi-layer root-zone soil moisture but also demonstrates reliable layer-wise forecasting up to 3 days ahead in a tropical, high-humidity Cocoa farm, providing a novel contribution to the existing literature. By offering robust, depth-resolved soil moisture predictions and short-term forecasts, this hybrid deep learning framework establishes a practical foundation for automated, climate-aware irrigation systems in perennial tropical crops. The results highlight how combining environmental feature engineering with advanced deep learning can strengthen data-driven decision support and enhance the resilience and sustainability of Cocoa production under Malaysia's humid, rainfall-variable climate.</div></div>","PeriodicalId":100262,"journal":{"name":"Climate Smart Agriculture","volume":"3 1","pages":"Article 100096"},"PeriodicalIF":0.0,"publicationDate":"2025-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145939624","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Vegetation type regulates the accumulation of plant- and microbial-derived carbon in mangrove wetlands 植被类型调节红树林湿地植物源碳和微生物源碳的积累

Climate Smart Agriculture

Pub Date : 2025-12-15 DOI: 10.1016/j.csag.2025.100095

Ziting Chen , Xing Liu , Qiuxia Chen , Sheng Yang , Xiaohu Yang , Zhenke Zhu , Shunbao Lu , Yongfu Li , Tida Ge , Shuang Wang

As pivotal blue carbon (C) ecosystems, mangrove wetlands play an integral role in climate change mitigation by sequestering substantial soil organic carbon (SOC). SOC comprises several key components, including plant-derived carbon (e.g., lignin phenols), microbial-derived C (e.g., amino sugars from necromass and glomalin-related soil proteins (GRSP), and mineral-associated C (e.g., Fe-bound OC). However, the contributions of these C fractions to SOC pool within mangrove ecosystems and the factors influencing these contributions remain unclear. Representative sampling sites were selected along the coastal gradient, encompassing bare mudflats (Mud), monospecific Spartina alterniflora stands (SA), mixed stands of S. alterniflora and Kandelia obovata (SK), pure Kandelia obovata stands (KO), and mixed stands of K. obovata and S. alterniflora (KS). We quantified the contributions of various C fractions to SOC and identified the key influencing factors through random forest analysis (RF) and partial least squares structural equation modelling (PLS-SEM). SOC increased from the mudflat to the embankment, and lignin phenols were the main contributor. Areas with high vegetation diversity (SK and KS) significantly enhanced the relative contributions of lignin phenols (47–48 %) and Fe-OC (124–137 %) to SOC, while reducing the microbial-derived C (12.2–22.2 %) contributions. This was particularly pronounced in KS. RF identified extracellular enzyme activity and iron oxides as the most critical factors regulating SOC and its fractions. PLS-SEM further elucidated that soil physicochemical properties exerted direct effects on lignin phenols and GRSP in the topsoil. In contrast, vegetation type indirectly influenced microbial-derived C and Fe-OC by primarily altering subsoil properties. This study provides a mechanistic understanding of how vegetation-mediated soil biogeochemistry regulate blue C sequestration in mangrove.

作为关键的蓝碳(C)生态系统，红树林湿地通过吸收大量的土壤有机碳（SOC），在减缓气候变化方面发挥着不可或缺的作用。有机碳包括几个关键成分，包括植物来源的碳（如木质素酚），微生物来源的碳（如坏死块和球小球素相关土壤蛋白（GRSP）中的氨基糖）和矿物相关的碳（如铁结合的OC）。然而，这些碳组分对红树林生态系统有机碳库的贡献及其影响因素尚不清楚。沿海岸梯度选择代表性样点，包括裸滩涂（Mud）、单种互花米草林（SA）、互花米草与倒花坎德尔林（SK）、纯倒花坎德尔林（KO）和倒花坎德尔林与互花米德尔林（KS）混交林。通过随机森林分析（RF）和偏最小二乘结构方程模型（PLS-SEM），量化了不同碳组分对土壤有机碳的贡献，并确定了关键影响因素。土壤有机碳由泥滩向堤岸逐渐增加，其中木质素酚类是主要贡献者。高植被多样性地区（SK和KS）显著提高了木质素酚类（47 ~ 48%）和Fe-OC（124 ~ 137%）对有机碳的相对贡献，降低了微生物源C（12.2 ~ 22.2%）的贡献。这在KS尤为明显。RF鉴定胞外酶活性和氧化铁是调控有机碳及其组分的最关键因子。PLS-SEM进一步阐明了土壤理化性质对表层土壤木质素酚和GRSP的直接影响。植被类型主要通过改变底土性质间接影响微生物碳和铁碳含量。本研究提供了植被介导的土壤生物地球化学如何调节红树林蓝碳固存的机制理解。

{"title":"Vegetation type regulates the accumulation of plant- and microbial-derived carbon in mangrove wetlands","authors":"Ziting Chen , Xing Liu , Qiuxia Chen , Sheng Yang , Xiaohu Yang , Zhenke Zhu , Shunbao Lu , Yongfu Li , Tida Ge , Shuang Wang","doi":"10.1016/j.csag.2025.100095","DOIUrl":"10.1016/j.csag.2025.100095","url":null,"abstract":"<div><div>As pivotal blue carbon (C) ecosystems, mangrove wetlands play an integral role in climate change mitigation by sequestering substantial soil organic carbon (SOC). SOC comprises several key components, including plant-derived carbon (e.g., lignin phenols), microbial-derived C (e.g., amino sugars from necromass and glomalin-related soil proteins (GRSP), and mineral-associated C (e.g., Fe-bound OC). However, the contributions of these C fractions to SOC pool within mangrove ecosystems and the factors influencing these contributions remain unclear. Representative sampling sites were selected along the coastal gradient, encompassing bare mudflats (Mud), monospecific <em>Spartina alterniflora</em> stands (SA), mixed stands of <em>S. alterniflora</em> and <em>Kandelia obovata</em> (SK), pure <em>Kandelia obovata</em> stands (KO), and mixed stands of <em>K. obovata</em> and <em>S. alterniflora</em> (KS). We quantified the contributions of various C fractions to SOC and identified the key influencing factors through random forest analysis (RF) and partial least squares structural equation modelling (PLS-SEM). SOC increased from the mudflat to the embankment, and lignin phenols were the main contributor. Areas with high vegetation diversity (SK and KS) significantly enhanced the relative contributions of lignin phenols (47–48 %) and Fe-OC (124–137 %) to SOC, while reducing the microbial-derived C (12.2–22.2 %) contributions. This was particularly pronounced in KS. RF identified extracellular enzyme activity and iron oxides as the most critical factors regulating SOC and its fractions. PLS-SEM further elucidated that soil physicochemical properties exerted direct effects on lignin phenols and GRSP in the topsoil. In contrast, vegetation type indirectly influenced microbial-derived C and Fe-OC by primarily altering subsoil properties. This study provides a mechanistic understanding of how vegetation-mediated soil biogeochemistry regulate blue C sequestration in mangrove.</div></div>","PeriodicalId":100262,"journal":{"name":"Climate Smart Agriculture","volume":"3 1","pages":"Article 100095"},"PeriodicalIF":0.0,"publicationDate":"2025-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145799022","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Balancing maize yield and greenhouse gas emissions: The integrated effects of no-till, straw return, and nitrogen rates 平衡玉米产量和温室气体排放：免耕、秸秆还田和施氮量的综合效应

Climate Smart Agriculture

Pub Date : 2025-12-14 DOI: 10.1016/j.csag.2025.100094

Na Li , Yan Huo , Zhi Dong , Zhe Zhang , Changzhen Tian , Huanan Zhou , Wei Han , Xue Liu , Guixia Zou , Jingru Zhao , John Yang , Zhanxiang Sun

Fertilizer application and tillage are well-known drivers of soil greenhouse gas (GHG) emissions. Most previous studies have focused on a single factor affecting GHG, but their interactive effects remain poorly understood. In arid or semi-arid region of northeastern China, straw returning is widely practiced but its combined influence with nitrogen (N) application on crop productivity and GHG is unclear. This study aimed to evaluate whether no-tillage with straw return could reduce nitrogen use and mitigate GHG emissions while maintaining maize yield. A two-year field experiment was conducted with five treatments: i) no straw return + conventional tillage (S0N1CT), ii) no straw return + no-tillage (S0N1NT), iii) full straw return + no N (SN0NT), iv) full straw return + 225 kg N ha⁻¹ + no-tillage (SN1NT), and v) full straw return + 335 kg N ha⁻¹ + no-tillage (SN2NT). Soil temperature, moisture, nitrate, GHG fluxes, and maize yield were measured and net ecosystem carbon budget (NECB), net global warming potential (NGWP), and carbon footprint (CF) calculated. Compared with S0N1CT, SN1NT treatment reduced cumulative CO₂, N₂O, and CH₄ emission by 7.9–10.0 %, 17.9−18.2 %, and 20.7−21.3 %, respectively. It also resulted in enhanced soil organic carbon (24.8−27.2 %), improved NECB (1322−1748 kg ha⁻¹), and decreased NGWP (11,400−12,480 kg ha⁻¹) and CF (0.06−0.08 kg ha⁻¹) while sustaining high grain yield (∼10,000 kg ha⁻¹). Full straw return under no-tillage with a moderate N rate (225 kg ha⁻¹) simultaneously improved carbon sequestration, reduced GHG emissions, and maintained high maize yield. This integrated management practice would be an effective strategy for climate-smart and sustainable agriculture production in the semi-arid regions of China.

施肥和耕作是众所周知的土壤温室气体排放的驱动因素。以前的大多数研究都集中在影响温室气体的单一因素上，但它们的相互作用仍然知之甚少。在中国东北干旱半干旱区，秸秆还田被广泛实施，但其与氮肥施用对作物生产力和温室气体排放的综合影响尚不清楚。本研究旨在评价秸秆还田免耕是否能在保持玉米产量的同时减少氮素利用和温室气体排放。为期2年的大田试验采用5个处理：1)不秸秆还田+常规耕作（S0N1CT）， 2)不秸秆还田+免耕（S0N1NT）， 3)全秸秆还田+免施氮（SN0NT）， 4)全秸秆还田+ 225 kg N ha−1 +免耕（SN1NT）， 5)全秸秆还田+ 335 kg N ha−1 +免耕（SN2NT）。测量了土壤温度、水分、硝态氮、温室气体通量和玉米产量，计算了净生态系统碳收支（NECB）、净全球变暖潜势（NGWP）和碳足迹（CF）。与S0N1CT相比，SN1NT处理的累积CO2、N2O和CH4排放量分别降低了7.9 ~ 10.0%、17.9 ~ 18.2%和20.7 ~ 21.3%。土壤有机碳（24.8 ~ 27.2%）增加，NECB （1322 ~ 1748 kg ha - 1）改善，NGWP （11400 ~ 12480 kg ha - 1）和CF （0.06 ~ 0.08 kg ha - 1）降低，同时保持高产量（~ 10000 kg ha - 1）。在中等施氮量（225 kg hm2 - 1）的免耕条件下，全秸秆还田同时提高了碳固存，减少了温室气体排放，并保持了玉米高产。这种综合管理实践将是中国半干旱地区气候智能型和可持续农业生产的有效战略。

{"title":"Balancing maize yield and greenhouse gas emissions: The integrated effects of no-till, straw return, and nitrogen rates","authors":"Na Li , Yan Huo , Zhi Dong , Zhe Zhang , Changzhen Tian , Huanan Zhou , Wei Han , Xue Liu , Guixia Zou , Jingru Zhao , John Yang , Zhanxiang Sun","doi":"10.1016/j.csag.2025.100094","DOIUrl":"10.1016/j.csag.2025.100094","url":null,"abstract":"<div><div>Fertilizer application and tillage are well-known drivers of soil greenhouse gas (GHG) emissions. Most previous studies have focused on a single factor affecting GHG, but their interactive effects remain poorly understood. In arid or semi-arid region of northeastern China, straw returning is widely practiced but its combined influence with nitrogen (N) application on crop productivity and GHG is unclear. This study aimed to evaluate whether no-tillage with straw return could reduce nitrogen use and mitigate GHG emissions while maintaining maize yield. A two-year field experiment was conducted with five treatments: i) no straw return + conventional tillage (S0N1CT), ii) no straw return + no-tillage (S0N1NT), iii) full straw return + no N (SN0NT), iv) full straw return + 225 kg N ha<sup>−1</sup> + no-tillage (SN1NT), and v) full straw return + 335 kg N ha<sup>−1</sup> + no-tillage (SN2NT). Soil temperature, moisture, nitrate, GHG fluxes, and maize yield were measured and net ecosystem carbon budget (NECB), net global warming potential (NGWP), and carbon footprint (CF) calculated. Compared with S0N1CT, SN1NT treatment reduced cumulative CO<sub>2</sub>, N<sub>2</sub>O, and CH<sub>4</sub> emission by 7.9–10.0 %, 17.9−18.2 %, and 20.7−21.3 %, respectively. It also resulted in enhanced soil organic carbon (24.8−27.2 %), improved NECB (1322−1748 kg ha<sup>−1</sup>), and decreased NGWP (11,400−12,480 kg ha<sup>−1</sup>) and CF (0.06−0.08 kg ha<sup>−1</sup>) while sustaining high grain yield (∼10,000 kg ha<sup>−1</sup>). Full straw return under no-tillage with a moderate N rate (225 kg ha<sup>−1</sup>) simultaneously improved carbon sequestration, reduced GHG emissions, and maintained high maize yield. This integrated management practice would be an effective strategy for climate-smart and sustainable agriculture production in the semi-arid regions of China.</div></div>","PeriodicalId":100262,"journal":{"name":"Climate Smart Agriculture","volume":"3 1","pages":"Article 100094"},"PeriodicalIF":0.0,"publicationDate":"2025-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145799021","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Climate-smart agriculture as disaster risk reduction: Effectiveness varies by hazard type in Pakistan 气候智慧型农业作为减少灾害风险：在巴基斯坦，效果因灾害类型而异

Climate Smart Agriculture

Pub Date : 2025-12-04 DOI: 10.1016/j.csag.2025.100093

Aziz Ahmed , Majid Mahar , Peters Egbedi , Amanullah Mahar , Md Ali Haider , Hafeez Ahmed Talpur , Sirilak Chaiboontha , Khalid Usman Shar , Habibullah Abbasi , Shakeel Ahmed Talpur

Climate change increasingly threatens agricultural systems globally, with Pakistan ranking among the most climate-vulnerable countries. This study systematically compared climate-smart agriculture (CSA) effectiveness across different disaster types in Sindh Province, Pakistan: riverine flooding (Dadu), drought (Tharparkar), and coastal flooding (Thatta). A multi-scale analysis combined climate data, area-weighted agricultural vulnerability assessment using 3889 district-crop yield records (2004–2023), and farmer surveys (n = 88). Climate analysis showed significant Arabian Sea warming (0.41 °C decade⁻¹, p < 0.01) with high precipitation variability (coefficient of variation ≈ 72 %). The corrected area-weighted vulnerability assessment ranked Tharparkar most vulnerable (#1, index: 66.23), followed by Thatta (#8, 31.69) and Dadu (#19, 24.77) among 23 districts. Drought analysis identified 2 of 20 years meeting drought criteria (Standardized Precipitation Index ≤ −0.5) in Tharparkar. CSA adoption varied significantly by disaster type (Kruskal-Wallis H = 74.06, p < 0.001). Strong positive correlation between CSA practices and disaster resilience emerged in drought-vulnerable Tharparkar (r = 0.756, p < 0.001, explaining 57 % of variance), while flood-prone districts showed negligible relationships (Thatta: r = −0.089, p > 0.05; Dadu: zero variance). Future projections indicate substantial sea-level rise of ∼331–751 mm by 2100 across Shared Socioeconomic Pathway scenarios. Results demonstrate CSA practices effectively reduce climate disaster risks in drought-prone systems, but effectiveness varies by disaster type, requiring tailored implementation approaches. This research provides the first systematic evidence comparing CSA effectiveness across different disaster contexts, supporting targeted rather than uniform adaptation policies.

气候变化日益威胁全球农业系统，巴基斯坦是最易受气候影响的国家之一。本研究系统地比较了巴基斯坦信德省不同灾害类型（河流洪水（Dadu）、干旱（Tharparkar）和沿海洪水（Thatta））下气候智慧型农业（CSA）的有效性。多尺度分析结合了气候数据、3889个地区作物产量记录（2004-2023）的面积加权农业脆弱性评估和农民调查（n = 88）。气候分析表明，阿拉伯海显著变暖（10 - 1年0.41°C, p < 0.01），降水变率高（变异系数≈72%）。修正后的面积加权脆弱性评价结果显示，在23个地区中，塔帕卡最脆弱（第1位，指数为66.23），其次是塔塔（第8位，指数为31.69）和大都（第19位，指数为24.77）。干旱分析发现，20年中有2年符合干旱标准（标准化降水指数≤- 0.5）。不同灾难类型的CSA采用率差异显著（Kruskal-Wallis H = 74.06, p < 0.001）。在干旱易发的Tharparkar （r = 0.756, p < 0.001，解释了57%的方差），而洪水易发地区的CSA实践与抗灾能力之间存在很强的正相关关系（Thatta: r = - 0.089, p < 0.05； Dadu：零方差）。未来的预测表明，在共享的社会经济路径情景下，到2100年海平面将大幅上升~ 331-751毫米。结果表明，CSA实践有效地降低了干旱易发系统的气候灾害风险，但效果因灾害类型而异，需要有针对性的实施方法。这项研究提供了第一个比较不同灾害背景下CSA有效性的系统证据，支持有针对性的而不是统一的适应政策。

{"title":"Climate-smart agriculture as disaster risk reduction: Effectiveness varies by hazard type in Pakistan","authors":"Aziz Ahmed , Majid Mahar , Peters Egbedi , Amanullah Mahar , Md Ali Haider , Hafeez Ahmed Talpur , Sirilak Chaiboontha , Khalid Usman Shar , Habibullah Abbasi , Shakeel Ahmed Talpur","doi":"10.1016/j.csag.2025.100093","DOIUrl":"10.1016/j.csag.2025.100093","url":null,"abstract":"<div><div>Climate change increasingly threatens agricultural systems globally, with Pakistan ranking among the most climate-vulnerable countries. This study systematically compared climate-smart agriculture (CSA) effectiveness across different disaster types in Sindh Province, Pakistan: riverine flooding (Dadu), drought (Tharparkar), and coastal flooding (Thatta). A multi-scale analysis combined climate data, area-weighted agricultural vulnerability assessment using 3889 district-crop yield records (2004–2023), and farmer surveys (<em>n</em> = 88). Climate analysis showed significant Arabian Sea warming (0.41 °C decade<sup>−1</sup>, <em>p</em> < 0.01) with high precipitation variability (coefficient of variation ≈ 72 %). The corrected area-weighted vulnerability assessment ranked Tharparkar most vulnerable (#1, index: 66.23), followed by Thatta (#8, 31.69) and Dadu (#19, 24.77) among 23 districts. Drought analysis identified 2 of 20 years meeting drought criteria (Standardized Precipitation Index ≤ −0.5) in Tharparkar. CSA adoption varied significantly by disaster type (Kruskal-Wallis <em>H</em> = 74.06, <em>p</em> < 0.001). Strong positive correlation between CSA practices and disaster resilience emerged in drought-vulnerable Tharparkar (<em>r</em> = 0.756, <em>p</em> < 0.001, explaining 57 % of variance), while flood-prone districts showed negligible relationships (Thatta: <em>r</em> = −0.089, <em>p</em> > 0.05; Dadu: zero variance). Future projections indicate substantial sea-level rise of ∼331–751 mm by 2100 across Shared Socioeconomic Pathway scenarios. Results demonstrate CSA practices effectively reduce climate disaster risks in drought-prone systems, but effectiveness varies by disaster type, requiring tailored implementation approaches. This research provides the first systematic evidence comparing CSA effectiveness across different disaster contexts, supporting targeted rather than uniform adaptation policies.</div></div>","PeriodicalId":100262,"journal":{"name":"Climate Smart Agriculture","volume":"3 1","pages":"Article 100093"},"PeriodicalIF":0.0,"publicationDate":"2025-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145718986","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The impacts of climate change on livestock: An interdisciplinary, scoping review of health, production, and adaptation strategies 气候变化对牲畜的影响：对健康、生产和适应战略的跨学科范围审查

Climate Smart Agriculture

Pub Date : 2025-11-01 DOI: 10.1016/j.csag.2025.100082

Alexandru Anuta , Xiuquan Wang , Pelin Kinay

Climate change has been recognized to negatively affect livestock animals, as it can severely impair their health and productivity by disrupting homeostasis. This study aims to linearly compile the sources of environmental stress on livestock animals into a more comprehensible format, which can be of great value to new policymakers, practitioners, or researchers alike. Literature curation was performed using online databases while focusing on publications made in the last 25 years. Unlike conventional reviews that tend to address single species or regional case studies, this paper integrates cross-species comparisons to identify shared physiological responses to heat stress and other climate-related stressors. It also contrasts the different temperature–humidity index (THI) standardization methods applied across livestock systems, providing one of the first interdisciplinary syntheses that unify animal physiology, biochemistry, and environmental physics under a single analytical framework. The main research gap addressed by our paper is the relative lack of acknowledgement in terms of the extent of climate stressors affecting livestock in the current literature. Previous work, to the best of our knowledge, does not address the entire radius of environmental stressors, which can range from increased temperatures to region-specific extreme weather events, such as dust storms. By linearly integrating insights from various fields of study, this paper serves as a valuable resource for any reader in the industry who is seeking to learn more about the challenges posed by climate change in the livestock sector, regardless of their experience or tenure.

气候变化已被认为对牲畜产生负面影响，因为它可以通过破坏体内平衡严重损害它们的健康和生产力。本研究旨在将家畜环境压力的来源线性地汇编成一种更容易理解的格式，这对新的政策制定者、从业者或研究人员都有很大的价值。文献管理是使用在线数据库进行的，重点是过去25年出版的出版物。与传统综述倾向于解决单一物种或区域案例研究不同，本文整合了跨物种比较，以确定对热应激和其他气候相关应激源的共同生理反应。它还对比了不同的温度-湿度指数（THI）标准化方法在畜牧业系统中的应用，提供了第一个跨学科综合，统一动物生理学，生物化学和环境物理学在一个单一的分析框架。本文解决的主要研究缺口是当前文献中对气候压力源影响牲畜的程度相对缺乏认识。据我们所知，以前的工作并没有解决环境压力源的整个半径，这些压力源可以从温度升高到特定地区的极端天气事件，如沙尘暴。通过线性整合来自各个研究领域的见解，本文为业内任何寻求更多了解气候变化对畜牧业构成的挑战的读者提供了宝贵的资源，无论他们的经验或任期如何。

{"title":"The impacts of climate change on livestock: An interdisciplinary, scoping review of health, production, and adaptation strategies","authors":"Alexandru Anuta , Xiuquan Wang , Pelin Kinay","doi":"10.1016/j.csag.2025.100082","DOIUrl":"10.1016/j.csag.2025.100082","url":null,"abstract":"<div><div>Climate change has been recognized to negatively affect livestock animals, as it can severely impair their health and productivity by disrupting homeostasis. This study aims to linearly compile the sources of environmental stress on livestock animals into a more comprehensible format, which can be of great value to new policymakers, practitioners, or researchers alike. Literature curation was performed using online databases while focusing on publications made in the last 25 years. Unlike conventional reviews that tend to address single species or regional case studies, this paper integrates cross-species comparisons to identify shared physiological responses to heat stress and other climate-related stressors. It also contrasts the different temperature–humidity index (THI) standardization methods applied across livestock systems, providing one of the first interdisciplinary syntheses that unify animal physiology, biochemistry, and environmental physics under a single analytical framework. The main research gap addressed by our paper is the relative lack of acknowledgement in terms of the extent of climate stressors affecting livestock in the current literature. Previous work, to the best of our knowledge, does not address the entire radius of environmental stressors, which can range from increased temperatures to region-specific extreme weather events, such as dust storms. By linearly integrating insights from various fields of study, this paper serves as a valuable resource for any reader in the industry who is seeking to learn more about the challenges posed by climate change in the livestock sector, regardless of their experience or tenure.</div></div>","PeriodicalId":100262,"journal":{"name":"Climate Smart Agriculture","volume":"2 4","pages":"Article 100082"},"PeriodicalIF":0.0,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145415991","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Ten-year rice management enhances soil aggregate stability in salt-alkali soils: Role of Fe/Al oxides and organic carbon 十年水稻管理提高盐碱地土壤团聚体稳定性：铁/铝氧化物和有机碳的作用

Climate Smart Agriculture

Pub Date : 2025-11-01 DOI: 10.1016/j.csag.2025.100085

Hao Hu , Qiuyun Wang , Panpan Gao , Luxin Zhang , Zhe Wei , Kelin Hu , Kaiqin Jiang , Jingbo Li , Haojie Feng , Shuwen Hu

Soil aggregates, the “structural skeleton” of the soil matrix, are fundamental to maintaining soil fertility, structural integrity, and ecosystem multifunctionality. Saline-alkali soils, which are widely distributed across arid and semi-arid regions worldwide, represent one of the most critical constraints to agricultural productivity. However, the mechanisms by which long-term rice management regulates aggregate stability in these soils remain poorly understood. To address this gap, we conducted a long-term field experiment in sodic saline-alkali soils of Baicheng, Jilin Province, northeastern China, including six treatments: rice management for 1, 2, 5, 8, and 10 years, and a non-cultivated control (CK). Soil samples (0–10 and 10–20 cm) were fractionated by aggregate size, and aggregate-associated SOC and Fe/Al oxides were quantified. Structural equation modeling (SEM) revealed that associations between long-term rice management and increased SOC and Fe/Al oxide contents, both of which were positively correlated with aggregate stability. Fe oxides were associated with the strongest positive correlations to stability, while soil salinity showed negative correlations. These findings suggest a hypothesized organic–inorganic synergistic cementation mechanism in which SOC (via encapsulation and biochemical bonding) and Fe/Al oxides (via mineral bridging) may jointly contribute to soil structure. This study deepens mechanistic understanding of soil physicochemical reconstruction in saline-alkali soils and provides a theoretical basis for sustainable saline soil rehabilitation under salinity stress.

土壤团聚体是土壤基质的“结构骨架”，是维持土壤肥力、结构完整性和生态系统多功能性的基础。盐碱地广泛分布在全球干旱和半干旱地区，是农业生产力最严重的制约因素之一。然而，长期水稻管理调节这些土壤中团聚体稳定性的机制仍然知之甚少。为了解决这一问题，我们在中国东北吉林省白城的钠盐碱土壤中进行了长期的田间试验，包括6个处理：水稻管理1、2、5、8和10年，以及一个非栽培对照（CK）。对0 ~ 10 cm和10 ~ 20 cm土壤样品按团聚体粒度进行分选，定量测定团聚体相关有机碳和Fe/Al氧化物含量。结构方程模型（SEM）显示，水稻长期经营与土壤有机碳和Fe/Al氧化物含量的增加呈正相关，两者均与团聚体稳定性呈正相关。铁氧化物与土壤稳定性呈显著正相关，土壤盐分与土壤稳定性呈显著负相关。这些发现表明，有机碳（通过包封和生化键合）和铁/铝氧化物（通过矿物桥接）可能共同促进土壤结构的有机-无机协同胶结机制。本研究加深了对盐碱地土壤理化重建机理的认识，为盐碱地在盐胁迫下的可持续恢复提供了理论依据。

{"title":"Ten-year rice management enhances soil aggregate stability in salt-alkali soils: Role of Fe/Al oxides and organic carbon","authors":"Hao Hu , Qiuyun Wang , Panpan Gao , Luxin Zhang , Zhe Wei , Kelin Hu , Kaiqin Jiang , Jingbo Li , Haojie Feng , Shuwen Hu","doi":"10.1016/j.csag.2025.100085","DOIUrl":"10.1016/j.csag.2025.100085","url":null,"abstract":"<div><div>Soil aggregates, the “structural skeleton” of the soil matrix, are fundamental to maintaining soil fertility, structural integrity, and ecosystem multifunctionality. Saline-alkali soils, which are widely distributed across arid and semi-arid regions worldwide, represent one of the most critical constraints to agricultural productivity. However, the mechanisms by which long-term rice management regulates aggregate stability in these soils remain poorly understood. To address this gap, we conducted a long-term field experiment in sodic saline-alkali soils of Baicheng, Jilin Province, northeastern China, including six treatments: rice management for 1, 2, 5, 8, and 10 years, and a non-cultivated control (CK). Soil samples (0–10 and 10–20 cm) were fractionated by aggregate size, and aggregate-associated SOC and Fe/Al oxides were quantified. Structural equation modeling (SEM) revealed that associations between long-term rice management and increased SOC and Fe/Al oxide contents, both of which were positively correlated with aggregate stability. Fe oxides were associated with the strongest positive correlations to stability, while soil salinity showed negative correlations. These findings suggest a hypothesized organic–inorganic synergistic cementation mechanism in which SOC (via encapsulation and biochemical bonding) and Fe/Al oxides (via mineral bridging) may jointly contribute to soil structure. This study deepens mechanistic understanding of soil physicochemical reconstruction in saline-alkali soils and provides a theoretical basis for sustainable saline soil rehabilitation under salinity stress.</div></div>","PeriodicalId":100262,"journal":{"name":"Climate Smart Agriculture","volume":"2 4","pages":"Article 100085"},"PeriodicalIF":0.0,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145465926","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Evaluating adaptive planting dates and elevated CO2 impacts on soybean yields under future climate scenarios 评估未来气候情景下适应性种植日期和CO2升高对大豆产量的影响

Climate Smart Agriculture

Pub Date : 2025-11-01 DOI: 10.1016/j.csag.2025.100083

Manavjot Singh , Xiaomao Lin , Vaishali Sharda

Crop simulation models depend on field-level management data such as planting dates, plant population, and selection of cultivar to capture yield responses under changing climate conditions. While many parameters exhibit relatively low variability from year to year, planting dates vary substantially due to weather conditions and individual management decisions. In the present study, the DSSAT CROPGRO-Soybean model was applied on a gridded scale to evaluate how a spring freeze probability-based early planting date strategy and elevated atmospheric CO₂ levels could mitigate the impacts of projected climate change on soybeans. The simulations incorporated projected climate data from six General Circulation Models (GCMs) under two shared socioeconomic pathways (SSPs), SSP2-4.5 and SSP5-8.5. Spring freeze probabilities were studied to derive location- and year-specific “adaptive planting dates”. Results indicated that elevated CO₂ significantly improved yield over the simulation period (2026–2100). However, the effectiveness of planting dates in mitigating the impact of climate change was statistically significant only under higher warming. When combined, the adaptive planting strategy and CO₂ fertilization improved yield by as much as 79 % relative to a fixed-planting, fixed-CO₂ scenario, although it remained below baseline yield levels. Further, the adaptive planting dates help increase the shortened days-to-anthesis period, with a more pronounced effect under SSP5-8.5. These findings highlight the potential of adjusting planting schedules and leveraging CO₂ fertilization to help offset climate-induced yield losses. Nevertheless, these strategies alone cannot entirely negate the climate change-driven yield declines; additional measures such as using longer-maturity group cultivars or breeding thermally resilient varieties may be necessary to sustain rainfed soybean production in the face of climate change.

作物模拟模型依赖于田间管理数据，如种植日期、植物种群和品种选择，以捕捉气候条件变化下的产量响应。虽然许多参数的年变异性相对较低，但由于天气条件和个人管理决策，种植日期变化很大。在本研究中，应用DSSAT CROPGRO-Soybean模型在网格尺度上评估了基于春季冻结概率的提前播种日期策略和大气CO2水平升高如何缓解预测的气候变化对大豆的影响。模拟采用了六个大气环流模式（GCMs）在两个共享社会经济路径（SSP2-4.5和SSP5-8.5）下的预估气候数据。研究了春季冻结的可能性，得出了特定地点和年份的“适应性种植日期”。结果表明，在模拟期间（2026-2100年），二氧化碳浓度升高显著提高了产量。然而，只有在气候变暖程度较高的情况下，种植日期在缓解气候变化影响方面的有效性才具有统计学意义。与固定种植、固定二氧化碳情景相比，适应性种植策略和二氧化碳施肥相结合可使产量提高79%，但仍低于基线产量水平。此外，适应种植日期对缩短的开花天数也有促进作用，且在SSP5-8.5条件下效果更为显著。这些发现强调了调整种植计划和利用二氧化碳施肥来帮助抵消气候导致的产量损失的潜力。然而，这些策略本身并不能完全抵消气候变化导致的产量下降；在气候变化的情况下，为了维持旱作大豆的生产，可能需要采取其他措施，如使用更成熟的群体品种或培育耐热品种。

{"title":"Evaluating adaptive planting dates and elevated CO2 impacts on soybean yields under future climate scenarios","authors":"Manavjot Singh , Xiaomao Lin , Vaishali Sharda","doi":"10.1016/j.csag.2025.100083","DOIUrl":"10.1016/j.csag.2025.100083","url":null,"abstract":"<div><div>Crop simulation models depend on field-level management data such as planting dates, plant population, and selection of cultivar to capture yield responses under changing climate conditions. While many parameters exhibit relatively low variability from year to year, planting dates vary substantially due to weather conditions and individual management decisions. In the present study, the DSSAT CROPGRO-Soybean model was applied on a gridded scale to evaluate how a spring freeze probability-based early planting date strategy and elevated atmospheric CO<sub>2</sub> levels could mitigate the impacts of projected climate change on soybeans. The simulations incorporated projected climate data from six General Circulation Models (GCMs) under two shared socioeconomic pathways (SSPs), SSP2-4.5 and SSP5-8.5. Spring freeze probabilities were studied to derive location- and year-specific “adaptive planting dates”. Results indicated that elevated CO<sub>2</sub> significantly improved yield over the simulation period (2026–2100). However, the effectiveness of planting dates in mitigating the impact of climate change was statistically significant only under higher warming. When combined, the adaptive planting strategy and CO<sub>2</sub> fertilization improved yield by as much as 79 % relative to a fixed-planting, fixed-CO<sub>2</sub> scenario, although it remained below baseline yield levels. Further, the adaptive planting dates help increase the shortened days-to-anthesis period, with a more pronounced effect under SSP5-8.5. These findings highlight the potential of adjusting planting schedules and leveraging CO<sub>2</sub> fertilization to help offset climate-induced yield losses. Nevertheless, these strategies alone cannot entirely negate the climate change-driven yield declines; additional measures such as using longer-maturity group cultivars or breeding thermally resilient varieties may be necessary to sustain rainfed soybean production in the face of climate change.</div></div>","PeriodicalId":100262,"journal":{"name":"Climate Smart Agriculture","volume":"2 4","pages":"Article 100083"},"PeriodicalIF":0.0,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145415978","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0