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学者姓名：毋亭
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Abstract ：
Environmental constraints such as drought and salt stress severely limit crop production worldwide. The WRKY TFs are very important in regulating plant growth and stress responses. Pineapple (Ananas comosus) is widely grown due to its unique flavor, high vitamins, and dietary fiber; however, the functional characterization of WRKY TFs in pineapple remains largely unexplored. In our study, we amplified and explored the molecular function of pineapple AcWRKY27, including its conserved domain, protein localization, transcriptional activity, and expression profiles in different tissues and stress treatments. Overexpression of AcWRKY27 in both rice and Arabidopsis thaliana (A. thaliana) resulted in growth inhibition, a decrease in primary root elongation, and a reduction in fresh weight under salt, drought, and ABA stress. RNA-Seq analysis and quantitative PCR (RT-qPCR) revealed that AcWRKY27 overexpression resulted in decreased expression levels of stress-responsive and ABA signaling pathway genes. These findings provide new perspectives on pineapple WRKY TFs and lay a foundation for improving pineapple stress tolerance through molecular breeding in the future.
Keyword ：
ABA ABA AcWRKY AcWRKY salt and drought stress salt and drought stress transcription factor transcription factor
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| GB/T 7714 | Chai, Gaifeng , Huang, Dongping , Gao, Jingai et al. Ectopic Expression of Pineapple AcWRKY27 Increases Sensitivity to Abiotic Stress in Rice and Arabidopsis [J]. | PHYSIOLOGIA PLANTARUM , 2025 , 177 (6) . |
| MLA | Chai, Gaifeng et al. "Ectopic Expression of Pineapple AcWRKY27 Increases Sensitivity to Abiotic Stress in Rice and Arabidopsis" . | PHYSIOLOGIA PLANTARUM 177 . 6 (2025) . |
| APA | Chai, Gaifeng , Huang, Dongping , Gao, Jingai , Wu, Ting , Xu, Lixin , Arabzai, Mohammad Gul et al. Ectopic Expression of Pineapple AcWRKY27 Increases Sensitivity to Abiotic Stress in Rice and Arabidopsis . | PHYSIOLOGIA PLANTARUM , 2025 , 177 (6) . |
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高精度土壤有机碳制图是研究耕地土壤有机碳时空格局及其影响机制的基础，相关研究结果可为农田“固碳减排”措施的制定提供决策支持。农业管理活动是农田土壤有机碳发生变化的重要影响因子，但基于农业管理活动的土壤有机碳制图却较为少见。基于遥感影像提取的物候参数是农业管理活动的直接反映，在研究农业管理活动对农田土壤有机碳的影响方面有较大应用潜力。基于此，本研究以福建省漳州市水稻田为研究对象，利用随机森林算法，基于5组不同的变量组合（A组：仅自然环境变量；B组：自然环境变量+早稻物候参数：C组：自然环境变量+晚稻物候参数；D组：自然环境变量+早稻物候参数+晚稻物候参数；E组：仅早稻物候参数+晚稻物候参数），分别构建土壤有机碳含量预测模型。通过对比5组模型的预测精度、预测值的空间分布特征和相关影响因子的重要性，分析物候参数对于土壤有机碳制图精度的影响作用，挖掘漳州市水田土壤有机碳制图的主要影响因子，解析对漳州市水田土壤有机碳有重要影响作用的农业管理活动。研究结果表明：物候参数的加入能够降低预测模型的误差和提升模型解释方差的能力；对漳州市水田土壤有机碳影响作用最大的物候参数依次为早稻季的NDVI增长速率（h1）、早稻生长季节开始的时间（a1）与早稻季NDVI下降速率（i1）；三个最重要的物候参数与土壤有机碳含量分别呈正相关、负相关和负相关，因此，采取能够促使早稻苗早生快发、加快早稻分蘖速率和减缓早稻衰老速率的水肥管理措施可增加耕地土壤有机碳含量。基于物候参数构建预测模型能有效提高农田土壤有机碳制图精度，基于物候参数的农田土壤有机碳制图研究可为农田管理提供决策支持，此次研究结果可为相关研究提供理论依据。
Keyword ：
农田土壤有机碳含量 农田土壤有机碳含量 农田管理措施 农田管理措施 数字土壤制图 数字土壤制图 物候参数 物候参数 随机森林 随机森林
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| GB/T 7714 | 吴启航 , 姚园 , 李一凡 et al. 福建省漳州市水稻物候特征对稻田土壤有机碳制图的影响 [J]. | 土壤学报 , 2024 , 61 (02) : 385-397 . |
| MLA | 吴启航 et al. "福建省漳州市水稻物候特征对稻田土壤有机碳制图的影响" . | 土壤学报 61 . 02 (2024) : 385-397 . |
| APA | 吴启航 , 姚园 , 李一凡 , 曹文琦 , 蔡欣瑶 , 毋亭 et al. 福建省漳州市水稻物候特征对稻田土壤有机碳制图的影响 . | 土壤学报 , 2024 , 61 (02) , 385-397 . |
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Abstract ：
气候变化对耕地土壤有机碳的影响机制研究是气候变化背景下耕地质量管理的基础.气候变化下作物物候的响应对耕地土壤有机碳也有重要影响.但目前的研究主要关注气候变化或气候变化下作物物候响应对耕地土壤有机碳的独立影响，鲜有研究分析气候变化与作物物候响应协同影响下耕地土壤有机碳的变化，以及区分量化二者对耕地土壤有机碳动态变化的贡献率.基于2008年和2021年耕地土壤表层样点数据、2008～2021年每年的季前和季中气候数据以及由2007～2022年增强植被指数时间序列提取的物候参数数据，利用随机森林算法构建土壤有机碳预测模型，通过模拟2008～2021年土壤有机碳的总变化、气候单独变化下的土壤有机碳变化、气候变化与作物物候响应协同影响下土壤有机碳的变化，区分量化了气候变化与气候变化下作物物候响应对耕地土壤有机碳变化的贡献率，并分析了福建省耕地土壤有机碳变化的优势影响因子及其空间分布.结果表明：(1)在气候变化与作物物候响应的协同影响下，2008～2021年福建省74.15%的耕地区域土壤有机碳呈减少态势，平均减少量为2.20g·kg-1,25.85%的耕地区域土壤有机碳呈增加态势，平均增加量为1.48 g·kg-1；(2)季前气候、气候变化下物候响应、季中气候和品种改变等农业管理措施调整下物候变化对土壤有机碳变化的平均贡献率依次为34.08%、28.56%、22.75%和14.61%，整体而言，气候变化对福建省耕地土壤有机碳变化的影响大于气候变化下作物物候的响应；(3)气候变化与气候变化下物候响应共同为优势影响因子的区域面积最大，占全省耕地面积的47.06%，其次为气候变化为优势影响因子的区域，面积占比为28.64%；(4)季前气候因子与品种改变等农业措施调整下物候变化对土壤有机碳变化贡献率较高的区域倾向于分布在高海拔地区，而季中气候因子与气候变化下物候响应对土壤有机碳变化贡献率较高的区域则倾向于分布在低海拔地区.相关研究结果能够为应对气候变化的耕地质量管理与粮食安全保护等政策的制定提供理论依据.
Keyword ：
作物生长 作物生长 数字土壤有机碳制图 数字土壤有机碳制图 机器学习 机器学习 气候变暖 气候变暖 物候变化 物候变化
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| GB/T 7714 | 李一凡 , 毋亭 , 姚园 et al. 气候变化与作物物候响应对福建省耕地土壤有机碳的影响 [J]. | 环境科学 , 2024 , 45 (10) : 6012-6027 . |
| MLA | 李一凡 et al. "气候变化与作物物候响应对福建省耕地土壤有机碳的影响" . | 环境科学 45 . 10 (2024) : 6012-6027 . |
| APA | 李一凡 , 毋亭 , 姚园 , 黎志强 , 沈金泉 , 翁怀楷 et al. 气候变化与作物物候响应对福建省耕地土壤有机碳的影响 . | 环境科学 , 2024 , 45 (10) , 6012-6027 . |
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Abstract ：
水稻是中国主要的粮食作物之一,实时且准确获取稻田区域及其空间分布特征是指导和管理农业生产的基础,对于保障国家粮食安全具有重要意义。但传统遥感变量与基于像元的机器学习分类算法在准确识别破碎度较高的稻田方面存在较大挑战。物候参数能够反映不同植被的生长动态,在识别稻田方面具有较大的应用潜力。面向对象的随机森林分类可以有效避免“椒盐”现象,提高稻田的分类精度。鉴于此,该研究以中国南方典型山地丘陵区——福建省漳州市稻田为研究对象,基于归一化植被指数、改进归一化水体指数、土壤调节植被指数、垂直极化后向散射系数、交叉极化后向散射系数和物候参数等多个遥感变量,利用面向对象的随机森林分类算法识别稻田,验证和分析物候参数与面向对象的随机森林分类法在提高南方复杂地形区稻田识别精度方面的有效性。结果表明:1)福建省漳州市稻田的最高识别精度为94.47%,Kappa系数为0.92,传统遥感变量、物候参数及面向对象的随机森林分类算法在准确识别破碎度较高的稻田方面具有协同优势;2)物候参数在表征植被生长与植被类型差异等方面具有显著优势,相较于仅基于传统遥感变量的试验组,物候参数与传统遥感变量的组合能够将稻田识别总体精度提高8.78～9.36个百分点;3)对于复杂地形区破碎度较高的稻田,面向对象的随机森林分类方法能够清晰明确地勾勒出稻田的形状与边界信息,且能够有效避免“椒盐”现象,相较于基于像元的分类方法,面向对象的分类法可将稻田识别精度提高0.58～1.53个百分点,因此,更适用于复杂地形区破碎农田的遥感提取。该研究结果可提高福建省漳州市稻田制图产品的应用价值,也可为中国南方复杂地形区稻田识别精度的进一步提高提供参考。
Keyword ：
时间序列 时间序列 物候参数 物候参数 稻田 稻田 遥感 遥感 随机森林 随机森林
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| GB/T 7714 | 姚园 , 毋亭 , 李一凡 et al. 基于水稻物候参数及面向对象算法的稻田识别 [J]. | 农业工程学报 , 2024 , 40 (11) : 150-158 . |
| MLA | 姚园 et al. "基于水稻物候参数及面向对象算法的稻田识别" . | 农业工程学报 40 . 11 (2024) : 150-158 . |
| APA | 姚园 , 毋亭 , 李一凡 , 黎志强 , 钱秀丽 , 张黎明 et al. 基于水稻物候参数及面向对象算法的稻田识别 . | 农业工程学报 , 2024 , 40 (11) , 150-158 . |
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Abstract ：
水稻是中国主要的粮食作物之一,实时且准确获取稻田区域及其空间分布特征是指导和管理农业生产的基础,对于保障国家粮食安全具有重要意义。但传统遥感变量与基于像元的机器学习分类算法在准确识别破碎度较高的稻田方面存在较大挑战。物候参数能够反映不同植被的生长动态,在识别稻田方面具有较大的应用潜力。面向对象的随机森林分类可以有效避免“椒盐”现象,提高稻田的分类精度。鉴于此,该研究以中国南方典型山地丘陵区——福建省漳州市稻田为研究对象,基于归一化植被指数、改进归一化水体指数、土壤调节植被指数、垂直极化后向散射系数、交叉极化后向散射系数和物候参数等多个遥感变量,利用面向对象的随机森林分类算法识别稻田,验证和分析物候参数与面向对象的随机森林分类法在提高南方复杂地形区稻田识别精度方面的有效性。结果表明:1)福建省漳州市稻田的最高识别精度为94.47%,Kappa系数为0.92,传统遥感变量、物候参数及面向对象的随机森林分类算法在准确识别破碎度较高的稻田方面具有协同优势;2)物候参数在表征植被生长与植被类型差异等方面具有显著优势,相较于仅基于传统遥感变量的试验组,物候参数与传统遥感变量的组合能够将稻田识别总体精度提高8.78~9.36个百分点;3)对于复杂地形区破碎度较高的稻田,面向对象的随机森林分类方法能够清晰明确地勾勒出稻田的形状与边界信息,且能够有效避免“椒盐”现象,相较于基于像元的分类方法,面向对象的分类法可将稻田识别精度提高0.58~1.53个百分点,因此,更适用于复杂地形区破碎农田的遥感提取。该研究结果可提高福建省漳州市稻田制图产品的应用价值,也可为中国南方复杂地形区稻田识别精度的进一步提高提供参考。
Keyword ：
时间序列 时间序列 物候参数 物候参数 稻田 稻田 遥感 遥感 随机森林 随机森林
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| GB/T 7714 | Yao, Yuan , Wu, Ting , Li, Yifan et al. 基于水稻物候参数及面向对象算法的稻田识别 [J]. | 农业工程学报 , 2024 , 40 (11) : 150-158 . |
| MLA | Yao, Yuan et al. "基于水稻物候参数及面向对象算法的稻田识别" . | 农业工程学报 40 . 11 (2024) : 150-158 . |
| APA | Yao, Yuan , Wu, Ting , Li, Yifan , Li, Zhiqiang , Qian, Xiuli , Zhang, Liming et al. 基于水稻物候参数及面向对象算法的稻田识别 . | 农业工程学报 , 2024 , 40 (11) , 150-158 . |
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Topographic variables derived from digital elevation models (DEM) are the most commonly used predictor in soil organic carbon (SOC) prediction. The predictability of these variates for the spatial heterogenity of SOC is influenced by the size of DEM resolution. This paper aimed to investigate the resolution effect of the topography at multiple DEM resolutions and select the most important predictors and the optimal resolution for SOC prediction in a large-scale and morphologically complex area. The study area, covering 15 600 km(2), was located in a mountainous province in China. A total of 3901 soil samples and three machine learning algorithms including random forest, gradient boosted regression tree, and artificial neural network were used to construct the SOC predictive models at resolutions of 30, 90, 150, 210, 270, 330, and 390 m. Topographic factors were derived from the DEM with an original resolution of 30 m and were subsequently resampled to varying resolutions by using the bilinear interpolation algorithm. The importance of each variate in each predictive model was computed for the most important predictor selection, and the determination coefficient (R-2) and Root Mean Square Error (RMSE) of each predictive model were computed for the accuracy evaluation. Comparative analysis on point-based topographic representation, the accuracy of predictive models, and the variable importance was conducted among different resolutions to investigate the resolution effect on the SOC prediction and to select the optimal resolution for this study area. The results showed that topography was the key factor influencing the SOC distribution in this large-scale area due to the region's mountainous nature, and the relative contribution of the topography to predict SOC distribution varied with DEM resolutions. Elevation was the most important topographic factor at all DEM resolutions. Accuracies of predictive models built on the three machine learning algorithms were all dependent on DEM resolution, indicating that DEM resolution had an important effect on SOC prediction. Overall, the random forest predictive models outperformed the other two algorithms, and its most accurate result was obtained at the 210-m resolution with an R-2 of 0.25 and an RMSE of 4.77 g kg(-1). In conclusion, we found that finer resolutions did not necessarily produce more accurate SOC predictions even in a large-scale and morphologically complex area. Therefore, investigating the resolution effect is suggested to select an optimal resolution for the SOC prediction in other regions characterized by similar geomorphological conditions as this study area.
Keyword ：
anthrosols anthrosols cultivated land cultivated land digital elevation model digital elevation model digital soil mapping digital soil mapping terrain terrain
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| GB/T 7714 | Wu, T. , Chen, J. Y. , Li, Y. F. et al. Resolution Effect of Soil Organic Carbon Prediction in a Large-Scale and Morphologically Complex Area [J]. | EURASIAN SOIL SCIENCE , 2023 , 56 (SUPPL 2) : S260-S275 . |
| MLA | Wu, T. et al. "Resolution Effect of Soil Organic Carbon Prediction in a Large-Scale and Morphologically Complex Area" . | EURASIAN SOIL SCIENCE 56 . SUPPL 2 (2023) : S260-S275 . |
| APA | Wu, T. , Chen, J. Y. , Li, Y. F. , Yao, Y. , Li, Z. Q. , Xing, S. H. et al. Resolution Effect of Soil Organic Carbon Prediction in a Large-Scale and Morphologically Complex Area . | EURASIAN SOIL SCIENCE , 2023 , 56 (SUPPL 2) , S260-S275 . |
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明确不同秸秆还田量对土壤“碳汇”的贡献大小是合理制定农业碳中和措施的基础。以我国典型亚热带地区——福建省水田土壤为研究对象，基于2016年15833个土壤样点实测数据和目前该地区最详细的1︰5万大比例尺土壤数据库，运用农业生态系统中广泛使用的DNDC(DeNitrification and DeComposition)模型模拟了不同秸秆还田率下全省未来的土壤有机碳动态变化。结果表明，2017—2053年传统管理（15%）以及秸秆还田30%、50%和90%下水田土壤的年均固碳速率分别为173、302、478和838 kg·hm~(-2)，固碳总量分别为11.56、20.15、31.90和55.95 Tg。从土壤亚类来看，咸酸和盐渍水稻土的年均固碳速率最大，不同秸秆还田率下介于220～920kg·hm~(-2)·a~(-1)之间；而渗育和潴育水稻土的固碳量最大，不同秸秆还田率下合计介于9.45～45.52 Tg之间，约占研究区总固碳量的81%。从行政区来看，龙岩、泉州两个地级市的固碳速率和总量均最大，不同秸秆还田率下均分别在202～937 kg·hm~(-2)·a~(-1)和1.55～8.34 Tg之间。总体而言，福建省水稻土亚类和行政区在不同秸秆还田率下的固碳潜力差异很大，应有针对性制定“固碳减排”管理措施。
Keyword ：
1︰5万数据库 1︰5万数据库 DNDC(DeNitrification and DeComposition)模型 DNDC(DeNitrification and DeComposition)模型 土壤有机碳 土壤有机碳 水田 水田 秸秆还田 秸秆还田
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| GB/T 7714 | 罗玉叶 , 邱龙霞 , 龙军 et al. 不同秸秆还田率情境下亚热带水田土壤的“碳汇”贡献模拟研究 [J]. | 土壤学报 , 2023 , 60 (05) : 1442-1455 . |
| MLA | 罗玉叶 et al. "不同秸秆还田率情境下亚热带水田土壤的“碳汇”贡献模拟研究" . | 土壤学报 60 . 05 (2023) : 1442-1455 . |
| APA | 罗玉叶 , 邱龙霞 , 龙军 , 陈瀚阅 , 毋亭 , 李晶 et al. 不同秸秆还田率情境下亚热带水田土壤的“碳汇”贡献模拟研究 . | 土壤学报 , 2023 , 60 (05) , 1442-1455 . |
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There is still an incomplete comprehension of the synergistic effects of two common meteorological, temperature (T) and precipitation (P), on soil organic carbon (SOC) stocks. The DeNitrification-DeComposition (DNDC) model is a process-based model for carbon (C) and nitrogen biogeochemistry in agricultural ecosystems and has been well validated. In this study, the DNDC model was applied to explore the synergistic contribution of T and P to SOC changes in paddy soils for 37 years on the southeast coast of China, Fujian Province, using the soil database with high-resolution at 1:50,000. We have found that the total SOC changes decreased by 11.59% (10.22 Tg C), and 19.72% (9.28 Tg C) from 2017 to 2053 under scenarios where T increases 2°C and P simultaneously increases and decreases by 20%, respectively, compared to the C sequestered in paddy soils under conventional management scenario with 11.56 Tg C. Results also indicated that the SOC changes in paddy soils along Fujian's southeast coast (e.g. Putian and Quanzhou) are more dramatic than those in inland northwest regions (e.g. Nanping and Sanming). Furthermore, as T increases, the C sequestration capacity of paddy soils declines steadily but becomes more sensitive to changes in P. Specifically, combining warming with P decrease accelerated SOC loss, but increased P might mitigate slightly the SOC loss induced by warming. However, paddy fields always had a long-term role in absorbing atmospheric CO2 under all scenarios in our study. Moreover, SOC changes within different paddy soil groups respond differently to P and T combined effects or applied separately. It is hoped that this study will contribute to a better understanding of soil carbon sequestration and help policymakers identify alternative scenarios for mitigating climate change and promoting a carbon-neutral sustainable development in China. © 2023, The Authors. All rights reserved.
Keyword ：
Climate change Climate change Ecosystems Ecosystems Organic carbon Organic carbon Soils Soils Uncertainty analysis Uncertainty analysis
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| GB/T 7714 | Li, Jing , Che, Yan , Xing, Shihe et al. Modeling Soil Organic Carbon Changes Under Multiple Meteorological Uncertainty Scenarios Using Dndc Model in Paddy Soils Along the Southeast Coast of China [J]. | SSRN , 2023 . |
| MLA | Li, Jing et al. "Modeling Soil Organic Carbon Changes Under Multiple Meteorological Uncertainty Scenarios Using Dndc Model in Paddy Soils Along the Southeast Coast of China" . | SSRN (2023) . |
| APA | Li, Jing , Che, Yan , Xing, Shihe , Zeng, Raymond Jianxiong , Liu, Licheng , Chen, Hanyue et al. Modeling Soil Organic Carbon Changes Under Multiple Meteorological Uncertainty Scenarios Using Dndc Model in Paddy Soils Along the Southeast Coast of China . | SSRN , 2023 . |
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明确未来温度升高下我国农田土壤的碳“源-汇”效应是合理制定碳中和管理政策的基础.以我国典型亚热带地区——整个福建省84个县(市、区)水田土壤为研究对象，基于目前该区域最详细的1∶5万大比例尺土壤数据库和生物地球化学过程模型(DNDC),模拟了2017～2053年不同温度升高情景下全省水田土壤的有机碳动态变化.结果表明，在常规温度(对照)以及温度上升2、4和6℃这4种情景分析下，福建省水田土壤的固碳总量分别为11.56、9.44、7.08和4.91 Tg,年均固碳速率(以C计)分别为173、141、106和74 kg·(hm~2·a)~(-1),说明随着未来温度升高固碳速率在下降，但总体而言在6℃升温下全省水田土壤仍是“碳汇”.从不同土壤类型来看，潜育水稻土受气温升高的影响最大，不同处理下固碳速率降幅介于20%～69%之间，而盐渍水稻土受气温升高的影响最小，不同处理下固碳速率降幅介于14%～43%之间.从不同行政区来看，受气温升高影响最大的是位于武夷山脉附近的三明市，不同处理下固碳速率降幅介于27%～83%之间，而受气温升高影响最小的是沿海的泉州市和莆田市，不同处理下固碳速率降幅分别介于10%～41%与14%～42%之间.总体来看，福建省各土壤类型和行政区的水田固碳速率因其本底土壤属性和气候环境等不同而对未来温度升高的响应程度差异很大.
Keyword ：
1∶5万土壤数据库 1∶5万土壤数据库 土壤有机碳 土壤有机碳 水稻土 水稻土 温度 温度 福建省 福建省
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| GB/T 7714 | 车燕 , 邱龙霞 , 吴凌云 et al. 福建省水田土壤有机碳积累对未来温度升高的响应 [J]. | 环境科学 , 2023 , 44 (05) : 2775-2785 . |
| MLA | 车燕 et al. "福建省水田土壤有机碳积累对未来温度升高的响应" . | 环境科学 44 . 05 (2023) : 2775-2785 . |
| APA | 车燕 , 邱龙霞 , 吴凌云 , 龙军 , 毋亭 , 李晶 et al. 福建省水田土壤有机碳积累对未来温度升高的响应 . | 环境科学 , 2023 , 44 (05) , 2775-2785 . |
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A soil organic carbon (SOC) map of high accuracy is the basis for taking mitigation measures against crises of food security and global climate change. Predicting SOC based on a limited number of soil samples can reduce the cost and time for laboratory analysis. This study aimed to assess the influence of sample size on the prediction of SOC and to identify the optimal sample size of SOC prediction for cropland in northern Jiangsu, China. A total of 1182 soil samples were randomly split into calibration and validation sets. Ten calibration subsets of samples between 108 and 1064 were selected by using a parent material-based stratified random resampling strategy. The random forest algorithm was used to develop 10 calibration models validated based on the same validation sample set. These 10 models were evaluated through the explained variance (EV) and the root mean square error (RMSE). The results showed that the calibration model based on 960 soil samples had the best performance in SOC prediction. Significantly biased predictions were produced by the calibration models based on more or less than 960 soil samples due to underrepresentation or overrepresentation. Relief and climate were demonstrated to be the predominant factors influencing SOC prediction in this study area. These results may provide theoretical support for studies relevant to SOC mapping.
Keyword ：
Cambisols Cambisols organic matter organic matter random forest random forest stratified random resampling stratified random resampling variable importance variable importance
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| GB/T 7714 | Wu, Ting , Wu, Qihang , Zhuang, Qianlai et al. Optimal Sample Size for SOC Content Prediction for Mapping Using the Random Forest in Cropland in Northern Jiangsu, China [J]. | EURASIAN SOIL SCIENCE , 2022 , 55 (12) : 1689-1699 . |
| MLA | Wu, Ting et al. "Optimal Sample Size for SOC Content Prediction for Mapping Using the Random Forest in Cropland in Northern Jiangsu, China" . | EURASIAN SOIL SCIENCE 55 . 12 (2022) : 1689-1699 . |
| APA | Wu, Ting , Wu, Qihang , Zhuang, Qianlai , Li, Yifan , Yao, Yuan , Zhang, Liming et al. Optimal Sample Size for SOC Content Prediction for Mapping Using the Random Forest in Cropland in Northern Jiangsu, China . | EURASIAN SOIL SCIENCE , 2022 , 55 (12) , 1689-1699 . |
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