Construction land transition (CLT) under rapid urbanization persistently alters regional land-use patterns, significantly affecting cropland allocation and agricultural production structures. As a critical indicator of safeguarding food security, staple crop diversity (SCD) is both essential for preserving biodiversity and for balancing its relationship with sustainable urbanization, yet its impact by CLT has been overlooked. To address this gap, we examined the influence of CLT on SCD across China from 2000 to 2020 by employing bivariate spatial autocorrelation and the spatial error model with lag dependence (SEMLD). The three most widely planted staple crops (rice, wheat, and maize) were selected, and levels of CLT and SCD were quantified by using multi-source spatial data. The results found that China's CLT levels rose from 0.097 to 0.126 during the study period, with a spatial distribution that decreased from east to west. Meanwhile, SCD increased gradually from 0.186 to 0.204, with the central region experiencing the most notable growth. The high SCD was observed in the east and low in the west, with high-value areas primarily concentrated in China's grain-producing regions. The rise in CLT corresponded with reduced SCD: Every 1% increase in CLT led to a national average 0.14% decrease in SCD, whereas heterogeneity analysis identified distinct spatial variations in the impact, characterized by an east-negative and west-positive pattern, and the negative impact was increasingly expanding. This study provides scientific insights for creating regionally differentiated control policies to optimize China's construction land transition, facilitating the coordination of protecting diverse staple food cropping systems and achieving effective land-use governance.
{"title":"Impact of Construction Land Transition on Staple Crop Diversity in China","authors":"Xiaowei Yao, Tian Yang, Divyani Kohli-Poll Jonker, Jaap Zevenbergen, Jie Zeng","doi":"10.1002/ldr.70503","DOIUrl":"https://doi.org/10.1002/ldr.70503","url":null,"abstract":"Construction land transition (CLT) under rapid urbanization persistently alters regional land-use patterns, significantly affecting cropland allocation and agricultural production structures. As a critical indicator of safeguarding food security, staple crop diversity (SCD) is both essential for preserving biodiversity and for balancing its relationship with sustainable urbanization, yet its impact by CLT has been overlooked. To address this gap, we examined the influence of CLT on SCD across China from 2000 to 2020 by employing bivariate spatial autocorrelation and the spatial error model with lag dependence (SEMLD). The three most widely planted staple crops (rice, wheat, and maize) were selected, and levels of CLT and SCD were quantified by using multi-source spatial data. The results found that China's CLT levels rose from 0.097 to 0.126 during the study period, with a spatial distribution that decreased from east to west. Meanwhile, SCD increased gradually from 0.186 to 0.204, with the central region experiencing the most notable growth. The high SCD was observed in the east and low in the west, with high-value areas primarily concentrated in China's grain-producing regions. The rise in CLT corresponded with reduced SCD: Every 1% increase in CLT led to a national average 0.14% decrease in SCD, whereas heterogeneity analysis identified distinct spatial variations in the impact, characterized by an east-negative and west-positive pattern, and the negative impact was increasingly expanding. This study provides scientific insights for creating regionally differentiated control policies to optimize China's construction land transition, facilitating the coordination of protecting diverse staple food cropping systems and achieving effective land-use governance.","PeriodicalId":203,"journal":{"name":"Land Degradation & Development","volume":"51 1","pages":""},"PeriodicalIF":4.7,"publicationDate":"2026-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147287593","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Land use change (LUC) from paddy rice to sugarcane cultivation strongly influences soil organic carbon (SOC) stocks, with the extent and direction of change depending on residue management and time since conversion. This study aimed to (i) evaluate SOC stock changes under different residue management practices and conversion periods following rice-to-sugarcane transition, and (ii) determine variations in the proportions of old rice-derived and new sugarcane-derived SOC. Eight sites were selected under two residue management practices—burned (B) and unburned (UB)—across four conversion periods: 1 year (SC1), 3 years (SC3), 5 years (SC5), and 10 years (SC10), with a paddy rice field as reference. Soil samples were collected from 0 to 20 and 20 to 40 cm depths. SOC stocks were measured, and δ13C analysis was used to track rice- and sugarcane-derived carbon. The interaction between residue management and conversion period significantly affected SOC stocks (p ≤ 0.05). Burned management resulted in significant SOC decreases in SC3 (4.90 Mg ha−1 topsoil; 3.18 Mg ha−1 subsoil) compared to the reference, whereas SOC under unburned management in SC5 did not differ significantly, indicating rapid recovery. δ13C analysis showed a sharp decline in rice-derived carbon within the first 3 years, stabilizing thereafter under both managements. Unburned residue enhanced the incorporation and early stabilization of sugarcane-derived carbon in SC3 and SC5.
从水稻种植到甘蔗种植的土地利用变化（LUC）强烈影响土壤有机碳（SOC）储量，其变化程度和方向取决于秸秆管理和转化后的时间。本研究旨在(i)评估水稻向甘蔗转化后不同残留管理措施和转化周期下土壤有机碳储量的变化，以及（ii）确定旧稻源和新甘蔗源有机碳比例的变化。以稻田为参照，选择了8个地点，采用燃烧(B)和未燃烧（UB）两种残留物管理方法，跨越4个转换期：1年（SC1）、3年（SC3）、5年（SC5）和10年（SC10）。土壤取样深度分别为0 ~ 20cm和20 ~ 40cm。测定了土壤有机碳储量，并利用δ13C分析对水稻和甘蔗碳源进行了追踪。残留管理与转化期的交互作用显著影响有机碳储量（p≤0.05）。与对照相比，燃烧管理导致SC3的有机碳含量显著降低（表层土壤4.90 Mg ha - 1，底土3.18 Mg ha - 1），而未燃烧管理下SC5的有机碳含量差异不显著，表明恢复迅速。δ13C分析表明，水稻碳在前3年内急剧下降，此后在两种管理下趋于稳定。未燃烧残渣促进了SC3和SC5中甘蔗衍生碳的掺入和早期稳定。
{"title":"Impact of Sugarcane Management Practices and Time Periods on Soil Organic Carbon and δ13C Signature After Paddy Rice Conversion","authors":"Nipon Mawan, Nuttapon Khongdee, Chunling Luo, Wanwisa Pansak","doi":"10.1002/ldr.70515","DOIUrl":"https://doi.org/10.1002/ldr.70515","url":null,"abstract":"Land use change (LUC) from paddy rice to sugarcane cultivation strongly influences soil organic carbon (SOC) stocks, with the extent and direction of change depending on residue management and time since conversion. This study aimed to (i) evaluate SOC stock changes under different residue management practices and conversion periods following rice-to-sugarcane transition, and (ii) determine variations in the proportions of old rice-derived and new sugarcane-derived SOC. Eight sites were selected under two residue management practices—burned (B) and unburned (UB)—across four conversion periods: 1 year (SC1), 3 years (SC3), 5 years (SC5), and 10 years (SC10), with a paddy rice field as reference. Soil samples were collected from 0 to 20 and 20 to 40 cm depths. SOC stocks were measured, and δ<sup>13</sup>C analysis was used to track rice- and sugarcane-derived carbon. The interaction between residue management and conversion period significantly affected SOC stocks (<i>p</i> ≤ 0.05). Burned management resulted in significant SOC decreases in SC3 (4.90 Mg ha<sup>−1</sup> topsoil; 3.18 Mg ha<sup>−1</sup> subsoil) compared to the reference, whereas SOC under unburned management in SC5 did not differ significantly, indicating rapid recovery. δ<sup>13</sup>C analysis showed a sharp decline in rice-derived carbon within the first 3 years, stabilizing thereafter under both managements. Unburned residue enhanced the incorporation and early stabilization of sugarcane-derived carbon in SC3 and SC5.","PeriodicalId":203,"journal":{"name":"Land Degradation & Development","volume":"51 1","pages":""},"PeriodicalIF":4.7,"publicationDate":"2026-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147330277","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Achieving sustainable and ecological utilization of farmland under the Greater Food Concept remains a critical challenge for farmland management in hilly and mountainous regions. This study proposes a theoretical framework to identify the integrated use of farmland (IUF) under the Greater Food Concept, which refers to a single parcel of farmland used for the cultivation of multiple crop and vegetable species through various cropping systems. Then, the study characterizes the spatial pattern of IUF and explores its impacts on the supply–demand relationship and eco-efficiency by taking Sichuan Province as the case study. Results showed that from 2010 to 2022, the total area of farmland exhibited a decreasing trend with spatial shrinkage, and there were 480.92 thousand ha of IUF in the study area. As the IUF could provide diverse crops for residents, it generated positive impacts on reducing the imbalance relationship between the supply and demand of farmland area under the Greater Food Concept. Also, the eco-efficiency of IUF in 2022 was 0.7615, which was much higher compared with the traditional utilization of farmland. The findings highlight that the IUF can be viewed as a feasible farming practice to guarantee farmland productivity, food security, and ecological sustainability. The IUF should be on the premise of strict farmland protection. Thus, alternative strategies involving the “quantity-quality-ecology” of farmland protection, the multi-utilization of farmland, and the subsidies for grain crop cultivation should also be recognized by policymakers to achieve sustainable farmland management and food security in hilly and mountainous areas.
{"title":"Toward Farmland Multifunctionality and Sustainability Under the Greater Food Concept: Exploring the Integrated Use of Farmland (IUF) in Hilly and Mountainous Areas","authors":"Yinhao Yao, Wenqiu Ma, Wenqing Li","doi":"10.1002/ldr.70519","DOIUrl":"https://doi.org/10.1002/ldr.70519","url":null,"abstract":"Achieving sustainable and ecological utilization of farmland under the Greater Food Concept remains a critical challenge for farmland management in hilly and mountainous regions. This study proposes a theoretical framework to identify the integrated use of farmland (IUF) under the Greater Food Concept, which refers to a single parcel of farmland used for the cultivation of multiple crop and vegetable species through various cropping systems. Then, the study characterizes the spatial pattern of IUF and explores its impacts on the supply–demand relationship and eco-efficiency by taking Sichuan Province as the case study. Results showed that from 2010 to 2022, the total area of farmland exhibited a decreasing trend with spatial shrinkage, and there were 480.92 thousand ha of IUF in the study area. As the IUF could provide diverse crops for residents, it generated positive impacts on reducing the imbalance relationship between the supply and demand of farmland area under the Greater Food Concept. Also, the eco-efficiency of IUF in 2022 was 0.7615, which was much higher compared with the traditional utilization of farmland. The findings highlight that the IUF can be viewed as a feasible farming practice to guarantee farmland productivity, food security, and ecological sustainability. The IUF should be on the premise of strict farmland protection. Thus, alternative strategies involving the “quantity-quality-ecology” of farmland protection, the multi-utilization of farmland, and the subsidies for grain crop cultivation should also be recognized by policymakers to achieve sustainable farmland management and food security in hilly and mountainous areas.","PeriodicalId":203,"journal":{"name":"Land Degradation & Development","volume":"25 1","pages":""},"PeriodicalIF":4.7,"publicationDate":"2026-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147287574","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Soil health is at risk from extreme weather, farming methods that harm the agroecosystem, and a loss of biodiversity. This makes it harder for ecosystems to recover and makes food security worse. Interactions between plants and microorganisms in the rhizosphere, particularly those involving nitrogen-fixing bacteria, arbuscular mycorrhizal fungi (AMF), and plant growth-promoting rhizobacteria (PGPR), are crucial for improving nutrient cycling, stress resistance, and soil structure. This review investigates the mechanistic and molecular underpinnings of these mutualistic connections, with a particular emphasis on the pivotal role of root exudates in regulating microbial recruitment and activity. Advancements in multi-omics and ecological modeling have shown geographical and temporal dynamic patterns in root-microbe interactions, providing novel insights into the formation of resilient and sustainable agroecosystems. The article examines innovative strategies, including synthetic microbial communities (SynComs), CRISPR-based microbial engineering, host-mediated microbiome selection, and precision inoculant delivery systems, as potential tools for restoring degraded lands, enhancing soil fertility, and developing climate-resilient agricultural systems. These integrative techniques collaborate to create synthetic holobionts, which are plant-microbiome capable of surviving in adverse environmental conditions. The impact of plant variety on microbial community composition is comprehensively examined, emphasizing functional redundancy and microbiome stability. This review presents a comprehensive framework for utilizing microbial breakthroughs to reduce reliance, restore soil health, enhance food security, and attain SDG-2030 in the context of global climate change.
{"title":"Next-Generation Approach and Mechanistic Insight Mediated Beneficial Plant-Microbe Interactions to Foster Resilient Agroecosystems and Sustain Soil Health","authors":"Sudhir Kumar Upadhyay, Prasann Kumar","doi":"10.1002/ldr.70521","DOIUrl":"https://doi.org/10.1002/ldr.70521","url":null,"abstract":"Soil health is at risk from extreme weather, farming methods that harm the agroecosystem, and a loss of biodiversity. This makes it harder for ecosystems to recover and makes food security worse. Interactions between plants and microorganisms in the rhizosphere, particularly those involving nitrogen-fixing bacteria, arbuscular mycorrhizal fungi (AMF), and plant growth-promoting rhizobacteria (PGPR), are crucial for improving nutrient cycling, stress resistance, and soil structure. This review investigates the mechanistic and molecular underpinnings of these mutualistic connections, with a particular emphasis on the pivotal role of root exudates in regulating microbial recruitment and activity. Advancements in multi-omics and ecological modeling have shown geographical and temporal dynamic patterns in root-microbe interactions, providing novel insights into the formation of resilient and sustainable agroecosystems. The article examines innovative strategies, including synthetic microbial communities (SynComs), CRISPR-based microbial engineering, host-mediated microbiome selection, and precision inoculant delivery systems, as potential tools for restoring degraded lands, enhancing soil fertility, and developing climate-resilient agricultural systems. These integrative techniques collaborate to create synthetic holobionts, which are plant-microbiome capable of surviving in adverse environmental conditions. The impact of plant variety on microbial community composition is comprehensively examined, emphasizing functional redundancy and microbiome stability. This review presents a comprehensive framework for utilizing microbial breakthroughs to reduce reliance, restore soil health, enhance food security, and attain SDG-2030 in the context of global climate change.","PeriodicalId":203,"journal":{"name":"Land Degradation & Development","volume":"190 1","pages":""},"PeriodicalIF":4.7,"publicationDate":"2026-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147287575","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Chenxi Liu, Manyu Dong, Qian Liu, Wenting Wang, Yulian Wang
High-accuracy rainfall erosivity (RE) data are essential for understanding soil erosion processes. However, considerable differences exist among various precipitation datasets in terms of RE estimation. This study aimed to develop a machine learning-based framework for multisource RE fusion to increase the accuracy and spatial consistency of RE estimation. To achieve this goal, RE data derived from station observations were used as a benchmark to systematically evaluate the performance characteristics of five commonly used gridded precipitation datasets (Climate Hazards Group InfraRed Precipitation with Station data [CHIRPS], CPC, CN05.1, CHM, and fifth-generation atmospheric reanalysis data from the European Centre for Medium-Range Weather Forecasts [ERA5]) in estimating RE across mainland China from 1983 to 2020, thereby identifying their biases. A machine learning-based multisource RE fusion framework was subsequently established by employing six algorithms, namely, linear regression (LR), decision tree regression (DTR), random forest (RF), extra trees regressor (ETR), extreme gradient boost (XGB), and gradient boosting machine (GBM), to integrate RE estimates from the above five data sources. The results revealed that (1) the station-interpolated precipitation products (CHM and CN05.1) outperformed the reanalysis dataset (ERA5), yet all five gridded datasets (CHIRPS, CPC, CN05.1, CHM, and ERA5) generally exhibited RE underestimation; (2) the fused RE product provided a significantly increased estimation accuracy, with the average root mean square error (RMSE) decreasing from 1826.21 to 1077.06 MJ mm ha−1 h−1 and the Nash–Sutcliffe efficiency (NSE) coefficient increasing from 0.67 to 0.89. The fusion method effectively corrected the common issues of the underestimation of high-intensity RE events (large and heavy RE) and the overestimation of moderate RE events observed in the five original datasets (CHIRPS, CPC, CN05.1, CHM, and ERA5); and (3) the fused RE dataset achieved significant improvements in complex and observation-sparse regions, such as the Tibetan Plateau (the average NSE increased from −0.30 to 0.60). Overall, the potential of machine learning-based fusion methods in enhancing RE estimation was demonstrated, and a high-resolution fused RE dataset that could serve as a reliable foundation for soil erosion assessment and land management was provided.
高精度的降雨侵蚀力（RE）数据对于了解土壤侵蚀过程至关重要。然而，不同降水资料集在RE估计方面存在较大差异。本研究旨在开发一种基于机器学习的多源RE融合框架，以提高RE估计的准确性和空间一致性。为实现这一目标，以台站观测所得的RE数据为基准，系统评价了5个常用网格降水数据集（Climate Hazards Group InfraRed precipitation with station data [CHIRPS]、CPC、CN05.1、CHM和欧洲中期天气预报中心的第五代大气再分析数据[ERA5]）估算1983 - 2020年中国大陆RE的性能特征。从而识别他们的偏见。随后，采用线性回归（LR）、决策树回归（DTR）、随机森林（RF）、额外树回归（ETR）、极端梯度增强（XGB）和梯度增强机（GBM） 6种算法，建立了基于机器学习的多源RE融合框架，对上述5个数据源的RE估计进行融合。结果表明：(1)站内插值降水产品（CHM和CN05.1）优于再分析数据集（ERA5），但5个格点数据集（CHIRPS、CPC、CN05.1、CHM和ERA5）普遍存在RE低估；(2)融合后的RE产品显著提高了估算精度，平均均方根误差（RMSE）从1826.21降低到1077.06 MJ mm ha−1 h−1，Nash-Sutcliffe效率（NSE）系数从0.67提高到0.89。融合方法有效地纠正了5个原始数据集（CHIRPS、CPC、CN05.1、CHM和ERA5）观测到的高强度RE事件（大RE和重RE）的低估和中等RE事件的高估的普遍问题；(3)融合后的RE数据集在青藏高原等复杂和观测稀疏地区取得了显著的改善（平均NSE从- 0.30增加到0.60）。总体而言，基于机器学习的融合方法在增强RE估计方面的潜力得到了证明，并为土壤侵蚀评估和土地管理提供了高分辨率融合RE数据集。
{"title":"Multi-Source Rainfall Erosivity Fusion Based on Machine Learning Models","authors":"Chenxi Liu, Manyu Dong, Qian Liu, Wenting Wang, Yulian Wang","doi":"10.1002/ldr.70496","DOIUrl":"https://doi.org/10.1002/ldr.70496","url":null,"abstract":"High-accuracy rainfall erosivity (RE) data are essential for understanding soil erosion processes. However, considerable differences exist among various precipitation datasets in terms of RE estimation. This study aimed to develop a machine learning-based framework for multisource RE fusion to increase the accuracy and spatial consistency of RE estimation. To achieve this goal, RE data derived from station observations were used as a benchmark to systematically evaluate the performance characteristics of five commonly used gridded precipitation datasets (Climate Hazards Group InfraRed Precipitation with Station data [CHIRPS], CPC, CN05.1, CHM, and fifth-generation atmospheric reanalysis data from the European Centre for Medium-Range Weather Forecasts [ERA5]) in estimating RE across mainland China from 1983 to 2020, thereby identifying their biases. A machine learning-based multisource RE fusion framework was subsequently established by employing six algorithms, namely, linear regression (LR), decision tree regression (DTR), random forest (RF), extra trees regressor (ETR), extreme gradient boost (XGB), and gradient boosting machine (GBM), to integrate RE estimates from the above five data sources. The results revealed that (1) the station-interpolated precipitation products (CHM and CN05.1) outperformed the reanalysis dataset (ERA5), yet all five gridded datasets (CHIRPS, CPC, CN05.1, CHM, and ERA5) generally exhibited RE underestimation; (2) the fused RE product provided a significantly increased estimation accuracy, with the average root mean square error (RMSE) decreasing from 1826.21 to 1077.06 MJ mm ha<sup>−1</sup> h<sup>−1</sup> and the Nash–Sutcliffe efficiency (NSE) coefficient increasing from 0.67 to 0.89. The fusion method effectively corrected the common issues of the underestimation of high-intensity RE events (large and heavy RE) and the overestimation of moderate RE events observed in the five original datasets (CHIRPS, CPC, CN05.1, CHM, and ERA5); and (3) the fused RE dataset achieved significant improvements in complex and observation-sparse regions, such as the Tibetan Plateau (the average NSE increased from −0.30 to 0.60). Overall, the potential of machine learning-based fusion methods in enhancing RE estimation was demonstrated, and a high-resolution fused RE dataset that could serve as a reliable foundation for soil erosion assessment and land management was provided.","PeriodicalId":203,"journal":{"name":"Land Degradation & Development","volume":"64 1","pages":""},"PeriodicalIF":4.7,"publicationDate":"2026-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147287592","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Yawen Dong, Shuanglin Chen, Ziwu Guo, Lili Fan, Jie Yang, Sheping Wang, Yuxin Li
Phosphorus (P) limitation is widespread in Moso bamboo (Phyllostachys edulis) forests and constrains growth and ecosystem functioning. Understanding how abandoned bamboo stands respond to changes in P availability during understory vegetation succession is critical for predicting ecosystem stability and informing sustainable management. We investigated abandoned Moso bamboo forests in southern Zhejiang Province, China, selecting sample plots representing early (0 years), mid (9 years), and late-successional (21 years) stages to examine foliar P allocation and its relationships with soil P fractions, soil chemical properties, and phosphatase activities. The results showed that at 9 years, total foliar P decreased while the foliar N:P ratio increased, indicating severe P limitation; bamboo responded by increasing the content and proportion of metabolic P. By 21 years, total foliar P and foliar lipid P had recovered, and P limitation was substantially alleviated. Soil organic P fractions (NaHCO3-Po, NaOH-Po, and C.HCl-Po) declined at 9 years but increased by 21 years, enhancing P bioavailability. Acidification and reduced phosphatase activities at 9 years constrained organic P mineralization, whereas elevated phosphatase activities at 21 years improved P availability. Partial least squares path modeling indicated that succession indirectly influenced foliar P allocation by modifying soil pH, total soil P, and phosphatase activity. Understory vegetation succession transformed abandoned bamboo stands from severe P limitation in the mid-successional stage to alleviated limitation in the late stage, mediated primarily by shifts in soil P fractions and their bioavailability. These findings provide mechanistic insights into P cycling in bamboo-dominated forests and inform strategies for restoring and sustainably managing degraded bamboo ecosystems.
{"title":"Soil Phosphorus Dynamics and Enzymatic Activity Drive Understory Vegetation Successional Shifts in Foliar Phosphorus Limitation in Abandoned Moso Bamboo Forests","authors":"Yawen Dong, Shuanglin Chen, Ziwu Guo, Lili Fan, Jie Yang, Sheping Wang, Yuxin Li","doi":"10.1002/ldr.70478","DOIUrl":"https://doi.org/10.1002/ldr.70478","url":null,"abstract":"Phosphorus (P) limitation is widespread in Moso bamboo (<i>Phyllostachys edulis</i>) forests and constrains growth and ecosystem functioning. Understanding how abandoned bamboo stands respond to changes in P availability during understory vegetation succession is critical for predicting ecosystem stability and informing sustainable management. We investigated abandoned Moso bamboo forests in southern Zhejiang Province, China, selecting sample plots representing early (0 years), mid (9 years), and late-successional (21 years) stages to examine foliar P allocation and its relationships with soil P fractions, soil chemical properties, and phosphatase activities. The results showed that at 9 years, total foliar P decreased while the foliar N:P ratio increased, indicating severe P limitation; bamboo responded by increasing the content and proportion of metabolic P. By 21 years, total foliar P and foliar lipid P had recovered, and P limitation was substantially alleviated. Soil organic P fractions (NaHCO<sub>3</sub>-P<sub>o</sub>, NaOH-P<sub>o</sub>, and C.HCl-P<sub>o</sub>) declined at 9 years but increased by 21 years, enhancing P bioavailability. Acidification and reduced phosphatase activities at 9 years constrained organic P mineralization, whereas elevated phosphatase activities at 21 years improved P availability. Partial least squares path modeling indicated that succession indirectly influenced foliar P allocation by modifying soil pH, total soil P, and phosphatase activity. Understory vegetation succession transformed abandoned bamboo stands from severe P limitation in the mid-successional stage to alleviated limitation in the late stage, mediated primarily by shifts in soil P fractions and their bioavailability. These findings provide mechanistic insights into P cycling in bamboo-dominated forests and inform strategies for restoring and sustainably managing degraded bamboo ecosystems.","PeriodicalId":203,"journal":{"name":"Land Degradation & Development","volume":"13 1","pages":""},"PeriodicalIF":4.7,"publicationDate":"2026-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147330269","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Semyon Bryanin, Anjelica Kondratova, Olga Piletskaya
Boreal larch forests remain vulnerable to disturbances that threaten their function as carbon sinks. We investigated the seasonal dynamics of soil respiration (Rs), litter respiration (RL), and enzyme activity in post-fire, clear-cut, and undisturbed larch forests in the discontinuous permafrost zone of Eastern Eurasia, 16 years after disturbance. We found that the effects of disturbances on Rs might persist for decades. During its seasonal peak in July, Rs was 1.5 to 2 times higher in disturbed than in undisturbed forests. In contrast to Rs, disturbances did not affect heterotrophic RL. Differences in enzyme activity in forest litter between post-fire and clear-cut sites, compared to undisturbed sites, were only observed in spring and autumn. The increase in Rs, but not in RL, indicates a greater influence of fire and clear-cutting on autotrophic and heterotrophic respiration in the mineral soil compared to microbial activity in the litter. In the forests on the permafrost, we observed altered biological parameters, including a summer increase in Rs, RL, and seasonal changes in the litter enzyme activity. Overall, the seasonal patterns of RL and enzyme activity are mediated by different ecological factors across the study sites. Temperature and pH are significant factors in undisturbed stands, while dissolved organic carbon becomes more prominent in disturbed sites. These insights are crucial for predicting carbon dynamics and the resilience of boreal ecosystems amid rising climate change and increasing disturbance frequency. Future research should integrate spatial and temporal scales to assess the long-term effects of disturbances in boreal forests.
{"title":"Seasonal Inconsistency: The Uneven Effect of Disturbances and Permafrost on Biogeochemical Processes in Larix gmelinii Forests of Eastern Eurasia","authors":"Semyon Bryanin, Anjelica Kondratova, Olga Piletskaya","doi":"10.1002/ldr.70483","DOIUrl":"https://doi.org/10.1002/ldr.70483","url":null,"abstract":"Boreal larch forests remain vulnerable to disturbances that threaten their function as carbon sinks. We investigated the seasonal dynamics of soil respiration (<i>R</i><sub>s</sub>), litter respiration (<i>R</i><sub>L</sub>), and enzyme activity in post-fire, clear-cut, and undisturbed larch forests in the discontinuous permafrost zone of Eastern Eurasia, 16 years after disturbance. We found that the effects of disturbances on <i>R</i><sub>s</sub> might persist for decades. During its seasonal peak in July, <i>R</i><sub>s</sub> was 1.5 to 2 times higher in disturbed than in undisturbed forests. In contrast to <i>R</i><sub>s</sub>, disturbances did not affect heterotrophic <i>R</i><sub>L</sub>. Differences in enzyme activity in forest litter between post-fire and clear-cut sites, compared to undisturbed sites, were only observed in spring and autumn. The increase in <i>R</i><sub>s</sub>, but not in <i>R</i><sub>L</sub>, indicates a greater influence of fire and clear-cutting on autotrophic and heterotrophic respiration in the mineral soil compared to microbial activity in the litter. In the forests on the permafrost, we observed altered biological parameters, including a summer increase in <i>R</i><sub>s</sub>, <i>R</i><sub>L</sub>, and seasonal changes in the litter enzyme activity. Overall, the seasonal patterns of <i>R</i><sub>L</sub> and enzyme activity are mediated by different ecological factors across the study sites. Temperature and pH are significant factors in undisturbed stands, while dissolved organic carbon becomes more prominent in disturbed sites. These insights are crucial for predicting carbon dynamics and the resilience of boreal ecosystems amid rising climate change and increasing disturbance frequency. Future research should integrate spatial and temporal scales to assess the long-term effects of disturbances in boreal forests.","PeriodicalId":203,"journal":{"name":"Land Degradation & Development","volume":"46 1","pages":""},"PeriodicalIF":4.7,"publicationDate":"2026-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147330276","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Drought poses a significant threat to agricultural productivity, particularly in rice‐growing regions. The use of multi‐source remote sensing satellites to monitor drought stress during the peak rice growing season remains largely unexplored. Therefore, this study employed integrating approaches such as Landsat‐8 and Sentinel‐2 imagery to monitor drought stress during the peak rice growing season (July–September) from 2021 to 2024 in Hunan province. Results of Landsat‐8 satellite indicate the Vegetation Health Index (VHI) identified extreme drought conditions (37.9%) in August during critical rice growth stages in 2022. The Normalized Difference Vegetation Index (NDVI) values decreased in September 2022. Similarly, the results of Actual Evapotranspiration (ETa) analysis showed elevated values in August 2022. Together, these indicators reveal a coupled response of vegetation stress and increased water demand during the peak rice growing period under severe drought conditions. Moreover, NDVI anomaly mapping demonstrated pronounced negative deviations (−40% to −20%) in July and August 2022, with vegetation stress persisting into September, confirming the lagged response of vegetation to drought conditions. While land use land cover (LULC) analysis revealed cropland area highly decreased from 2022 to 2024 by 8.68%, built‐up areas expanded continuously from 2021 to 2024, increasing by 13.1%. Tree covered area remained relatively stable; thus, a negligible change was noted by 0.87% during the study period. In conclusion, the integrated multi‐indicator approach effectively determined the spatiotemporal drought impacts on total cropland and effects on the peak rice growing season in 2022 during the entire studied period. These findings provide valuable understanding for developing drought early warning systems and informing water management strategies in rice production systems vulnerable to climate variability.
{"title":"Machine Learning‐Based Drought Stress Mapping Using Landsat and Sentinel‐2 Imagery: A Remote Sensing Approach","authors":"Ziqi Fang, Jiayi Zhu, Xiaoqian Liao, Lei Zhang","doi":"10.1002/ldr.70509","DOIUrl":"https://doi.org/10.1002/ldr.70509","url":null,"abstract":"Drought poses a significant threat to agricultural productivity, particularly in rice‐growing regions. The use of multi‐source remote sensing satellites to monitor drought stress during the peak rice growing season remains largely unexplored. Therefore, this study employed integrating approaches such as Landsat‐8 and Sentinel‐2 imagery to monitor drought stress during the peak rice growing season (July–September) from 2021 to 2024 in Hunan province. Results of Landsat‐8 satellite indicate the Vegetation Health Index (VHI) identified extreme drought conditions (37.9%) in August during critical rice growth stages in 2022. The Normalized Difference Vegetation Index (NDVI) values decreased in September 2022. Similarly, the results of Actual Evapotranspiration (ETa) analysis showed elevated values in August 2022. Together, these indicators reveal a coupled response of vegetation stress and increased water demand during the peak rice growing period under severe drought conditions. Moreover, NDVI anomaly mapping demonstrated pronounced negative deviations (−40% to −20%) in July and August 2022, with vegetation stress persisting into September, confirming the lagged response of vegetation to drought conditions. While land use land cover (LULC) analysis revealed cropland area highly decreased from 2022 to 2024 by 8.68%, built‐up areas expanded continuously from 2021 to 2024, increasing by 13.1%. Tree covered area remained relatively stable; thus, a negligible change was noted by 0.87% during the study period. In conclusion, the integrated multi‐indicator approach effectively determined the spatiotemporal drought impacts on total cropland and effects on the peak rice growing season in 2022 during the entire studied period. These findings provide valuable understanding for developing drought early warning systems and informing water management strategies in rice production systems vulnerable to climate variability.","PeriodicalId":203,"journal":{"name":"Land Degradation & Development","volume":"47 1","pages":""},"PeriodicalIF":4.7,"publicationDate":"2026-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146778640","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Land degradation affects billions of people worldwide and causes massive economic losses. India faces severe pressure from its rising population, urbanization, and climate change; hence committing to restore 26 million hectares of degraded land by 2030 under its Bonn challenge and UNCCD commitments. This review examines India's restoration policies, measures progress toward the 2030 target, and identifies key implementation challenges. It combines policy analysis and case studies of sites like the Aravali Biodiversity Park and Suryakunj. So far, the country has restored 18.94 Mha, which is roughly 73% of its 2030 goals. This has been achieved as a result of national level programmes like the Green India Mission, Twenty Point Program, and the Compensatory Afforestation Fund Management and Planning Authority. These have increased the forest cover by 15,891 km2 from 2013 to 2021. However, significant challenges remain. Coordination between government agencies, funding delays, weak monitoring systems, and limited community involvement reduce program effectiveness. Many projects rely on fast-growing non-native trees that may harm biodiversity and soil health over time. Successful local examples show that community participation and native species produce better results. The study concludes that India can meet its 2030 restoration target but needs better coordination between programs, stronger community engagement, improved monitoring focused on ecological outcomes rather than just area covered, and wider use of ecosystem-based approaches. Success depends on addressing implementation gaps while building on existing achievements rather than starting completely new approaches.
{"title":"India's 2030 Forest Restoration Goals: A Critical Review of Policies and Progress","authors":"Aditi Mishra, Sachin Uniyal, Indra D. Bhatt","doi":"10.1002/ldr.70493","DOIUrl":"https://doi.org/10.1002/ldr.70493","url":null,"abstract":"Land degradation affects billions of people worldwide and causes massive economic losses. India faces severe pressure from its rising population, urbanization, and climate change; hence committing to restore 26 million hectares of degraded land by 2030 under its Bonn challenge and UNCCD commitments. This review examines India's restoration policies, measures progress toward the 2030 target, and identifies key implementation challenges. It combines policy analysis and case studies of sites like the Aravali Biodiversity Park and Suryakunj. So far, the country has restored 18.94 Mha, which is roughly 73% of its 2030 goals. This has been achieved as a result of national level programmes like the Green India Mission, Twenty Point Program, and the Compensatory Afforestation Fund Management and Planning Authority. These have increased the forest cover by 15,891 km<sup>2</sup> from 2013 to 2021. However, significant challenges remain. Coordination between government agencies, funding delays, weak monitoring systems, and limited community involvement reduce program effectiveness. Many projects rely on fast-growing non-native trees that may harm biodiversity and soil health over time. Successful local examples show that community participation and native species produce better results. The study concludes that India can meet its 2030 restoration target but needs better coordination between programs, stronger community engagement, improved monitoring focused on ecological outcomes rather than just area covered, and wider use of ecosystem-based approaches. Success depends on addressing implementation gaps while building on existing achievements rather than starting completely new approaches.","PeriodicalId":203,"journal":{"name":"Land Degradation & Development","volume":"1 1","pages":""},"PeriodicalIF":4.7,"publicationDate":"2026-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147287576","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Kemal Adem, Esra Kavalcı Yılmaz, Pelin Alaboz, Orhan Dengiz, Fikret Saygın
Soil erodibility (USLE‐K) is a critical indicator of soil susceptibility to erosion; however, its accurate estimation remains challenging due to complex and nonlinear interactions among soil physicochemical properties. Most existing studies focus either on traditional machine learning techniques or on individual deep learning models, often addressing classification or regression tasks separately and rarely integrating spatial validation of predictive results. The present study addresses these limitations by proposing an integrated comparative framework that simultaneously evaluates machine learning and advanced deep learning algorithms for both classification and continuous prediction of soil erodibility. Random Forest (RF) was compared with deep learning architectures including 1D CNN, CNN + LSTM, TabNet, RNN, LSTM, and GRU using a large dataset derived from field measurements and laboratory soil analyses in the Sakarya Basin. Model performance was assessed using standard classification and regression metrics, while predictive outputs were further evaluated through geostatistical interpolation to examine spatial consistency. The TabNet classifier achieved the highest classification accuracy (94%), demonstrating its effectiveness in capturing complex feature interactions in tabular soil data. For regression‐based prediction, the GRU model exhibited superior performance with an R2 value of 94%, outperforming both other deep learning models and the classical RF approach. Statistical analyses indicated that regression‐based deep learning models produced largely similar predictions, a finding supported by strong agreement in spatial distribution maps. The added value of this study lies in its combined evaluation of predictive accuracy and spatial coherence, demonstrating that deep learning models—particularly TabNet and GRU—provide reliable and spatially consistent soil erodibility assessments. The proposed framework offers a practical decision‐support tool for soil erosion risk mapping, land‐use planning, and the development of effective soil conservation and erosion control strategies in heterogeneous environmental systems.
{"title":"Comparative Analysis of Regression and Classification‐Based Deep Learning and Machine Learning Models for Soil Erodibility Prediction","authors":"Kemal Adem, Esra Kavalcı Yılmaz, Pelin Alaboz, Orhan Dengiz, Fikret Saygın","doi":"10.1002/ldr.70518","DOIUrl":"https://doi.org/10.1002/ldr.70518","url":null,"abstract":"Soil erodibility (USLE‐K) is a critical indicator of soil susceptibility to erosion; however, its accurate estimation remains challenging due to complex and nonlinear interactions among soil physicochemical properties. Most existing studies focus either on traditional machine learning techniques or on individual deep learning models, often addressing classification or regression tasks separately and rarely integrating spatial validation of predictive results. The present study addresses these limitations by proposing an integrated comparative framework that simultaneously evaluates machine learning and advanced deep learning algorithms for both classification and continuous prediction of soil erodibility. Random Forest (RF) was compared with deep learning architectures including 1D CNN, CNN + LSTM, TabNet, RNN, LSTM, and GRU using a large dataset derived from field measurements and laboratory soil analyses in the Sakarya Basin. Model performance was assessed using standard classification and regression metrics, while predictive outputs were further evaluated through geostatistical interpolation to examine spatial consistency. The TabNet classifier achieved the highest classification accuracy (94%), demonstrating its effectiveness in capturing complex feature interactions in tabular soil data. For regression‐based prediction, the GRU model exhibited superior performance with an <jats:italic>R</jats:italic> <jats:sup>2</jats:sup> value of 94%, outperforming both other deep learning models and the classical RF approach. Statistical analyses indicated that regression‐based deep learning models produced largely similar predictions, a finding supported by strong agreement in spatial distribution maps. The added value of this study lies in its combined evaluation of predictive accuracy and spatial coherence, demonstrating that deep learning models—particularly TabNet and GRU—provide reliable and spatially consistent soil erodibility assessments. The proposed framework offers a practical decision‐support tool for soil erosion risk mapping, land‐use planning, and the development of effective soil conservation and erosion control strategies in heterogeneous environmental systems.","PeriodicalId":203,"journal":{"name":"Land Degradation & Development","volume":"32 1","pages":""},"PeriodicalIF":4.7,"publicationDate":"2026-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146778642","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：