Scientia Agricultura Sinica ›› 2025, Vol. 58 ›› Issue (8): 1564-1578.doi: 10.3864/j.issn.0578-1752.2025.08.008

• SOIL & FERTILIZER·WATER-SAVING IRRIGATION·AGROECOLOGY & ENVIRONMENT • Previous Articles     Next Articles

Simulating Soil Organic Carbon Dynamic Changes in Dryland and Paddy Field of Northeast China Using RothC Model

ZHANG HaoXin1(), YU ShengYue1, LEI QiuLiang1(), DU XinZhong1, ZHANG Jizong1, AN MiaoYing1, FAN BingQian1, LUO JiaFa2, LIU HongBin1   

  1. 1 Key Laboratory of Non-Point Source Pollution Control, Ministry of Agriculture and Rural Affairs/Changping Soil Quality National Observation and Research Station/State Key Laboratory of Efficient Utilization of Arid and Semi-Arid Arable Land in Northern China/Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China
    2 AgResearch Ruakura Research Centre, 10 Bisley Road, Hamilton 3214, New Zealand
  • Received:2024-07-25 Accepted:2024-09-19 Online:2025-04-16 Published:2025-04-21
  • Contact: LEI QiuLiang

RichHTML

6

PDF

52

Abstract:

【Objective】 This study explored the applicability of the RothC model for simulating soil organic carbon (SOC) dynamics in dryland and paddy fields in Northeast China and evaluated the impact of various calibration methods on simulation performance.【Method】 This study selected one typical dryland and one typical paddy field as long-term experimental sites. The dryland experiment was conducted at the Heilongjiang Agricultural Ecology Experimental Station of the Chinese Academy of Sciences (2004-2015), and the paddy field experiment utilized data from the 850 Farm (2010-2017). At each experimental site, two treatments were selected for model simulation validation and performance evaluation: one with fertilization only, without straw return (NPK), and the other with both fertilization and straw returning (NPKS). For the paddy field soil, in addition to the RothC model, two modified versions, including RothC_p and RothC_0.6, were also selected for suitability evaluation. Three different model calibration methods were employed: the equilibrium method, parameter optimization method, and transfer function method, to analyze the impact of these calibration methods on model simulation performance. Normalized root mean square error (nRMSE), mean difference (MD), and the index of agreement (d) were selected as model evaluation metrics. 【Result】At the Heilongjiang station, organic carbon input exhibited a significant fluctuating trend, with the average annual carbon input under NPK and NPKS treatments being 1.71 and 3.52 t·hm-², respectively. In contrast, organic carbon input at the 850 Farm was relatively stable, with the average annual carbon input for NPK and NPKS treatments being 1.89 and 5.90 t·hm-², respectively. The simulation validation results from the Heilongjiang station showed that, under different model calibration methods, the nRMSE was consistently below 5%, and the index of agreement (d) ranged from 0.60 to 0.74. This indicated that the model performance was excellent across all calibration methods, and RothC was able to accurately simulate the SOC stock changes for both NPK and NPKS treatments in the dryland. When using the M2 method, the nRMSE for NPK and NPKS was the smallest, at 3.46% and 3.09%, respectively. The simulation validation results for the 850 Farm showed that the MD for RothC and RothC_p ranged from -1.47 to -13.41, with nRMSE values between 2.90% and 26.48% and d-values all below 0.1. This indicated that both models significantly overestimated the increase in SOC stocks and were unable to accurately simulate the changes in SOC stocks in the paddy field. For the RothC_0.6 model under the NPK treatment, the MD ranged from -0.08 to 0.44, with nRMSE values between 0.24% and 0.85% and d-values ranging from 0.31 to 0.76. Under the NPKS treatment, the MD ranged from -5.71 to -6.22, with nRMSE values between 11.21% and 12.12% and d-values between 0.12 and 0.13. These results indicated that RothC_0.6 could accurately simulate the dynamic changes in SOC stocks under the NPK treatment but significantly overestimate the changes in SOC stocks under the NPKS treatment.【Conclusion】RothC and RothC_0.6 were suitable for studying the dynamic changes in SOC stocks under dryland and paddy field conditions without straw returning in the Northeast region, respectively, and could accurately simulate the trends in SOC stocks. The impact of different model calibration methods on simulation performance was not significant. However, the transfer function method was simpler to compute, saved model running time, and provided better simulation performance. Therefore, this study recommended prioritizing the use of the transfer function method for model calibration.

Key words: RothC model, SOC, straw return, model calibration methods, dryland, paddy, Northeast China

Fig. 1

Monthly mean temperature, precipitation and ET0 of Hailun station(a) and Farm 850 (b)"

Table 1

Basic physical and chemical properties of soil"

站点
Site		 试验处理
Treatment		 有机质
SOM (g·kg-1)		 全氮
Total N (g·kg-1)		 全磷
Total P (g·kg-1)		 全钾
Total K (g·kg-1)		 黏粒含量
Clay (%)		 容重
Bulk density (g·cm-3)		 pH
海伦站
Hailun station		 NPK 47.4 2.35 0.73 17.79 42.39 1.12 5.79
NPKS 45.9 2.33 0.73 19.47 42.76 1.21 5.86
八五〇农场
850 Farm		 NPK 34.66 1.83 0.74 19.10 32.50 1.29 5.48
NPKS 34.66 1.75 0.73 18.20 32.50 1.29 5.75

Table 2

Calculation parameters for different crop residues"

作物 Crop PRS Pstubble Cplant (g·kg-1) 参考文献Reference
玉米 Maize 0.351 0.03 444 [27]
大豆 Soybean 0.317 0.10 420 [28-29]
水稻 Rice 0.200 0.10 400 [30]

Fig. 2

Annual carbon inputs for each treatment at Hailun station (a) and Farm 850 (b)"

Table 3

Allocation of each carbon pool under different calibration methods"

站点
Site		 处理
Treatment		 校准方法
Calibration		 各碳库含量 Content of each carbon pool (t·hm-2) 总有机碳
TSOC
(t·hm-2)
易分解碳库
DPM		 难分解碳库
RPM		 微生物碳库
BIO		 腐殖化碳库
HUM		 惰性碳库
IOM
海伦站
Hailun station		 NPK M1 1.37 7.86 1.17 45.82 5.35 61.58
M2 0.75 7.41 1.14 46.94 5.35 61.58
M3 0.02 7.42 1.09 47.71 5.35 61.58
NPKS M1 1.43 8.18 1.23 47.72 5.61 64.16
M2 0.78 7.71 1.18 48.88 5.61 64.16
M3 0.01 7.72 1.13 49.69 5.61 64.16
八五〇农场
Farm 805		 NPK M1 0.61 6.7 1.01 39.07 4.39 51.78
M2 0.3 6.64 1.02 39.42 4.39 51.78
M3 0.03 6.44 0.9 40.01 4.39 51.78
NPKS M1 0.61 6.7 1.01 39.07 4.39 51.78
M2 0.3 6.64 1.02 39.42 4.39 51.78
M3 0.03 6.44 0.9 40.01 4.39 51.78

Fig. 3

Comparison between simulated and measured values using different model calibration methods at Hailun station"

Fig. 4

Correlation between simulated values and measured values of different model calibration methods at Hailun station"

Table 4

Evaluation index of model performance of RothC model with different model calibration methods at Hailun station"

处理Treatment 模型Model 校准方法Calibration MD nRMSE (%) d
NPK RothC M1 0.22 3.51 0.69
M2 -0.43 3.46 0.67
M3 -0.99 3.89 0.60
NPKS RothC M1 0.56 3.27 0.70
M2 -0.12 3.09 0.74
M3 -0.70 3.32 0.72

Fig. 5

Comparison of simulated values and measured values with different model calibration methods in Farm 850"

Fig. 6

Correlation of simulated values and measured values with different model calibration methods in Farm 850"

Table 5

Evaluation index of model performance of RothC model with different model calibration methods at Farm 850"

处理Treatment 模型Model 校准方法Calibration MD nRMSE(%) d
NPK RothC M1 -1.47 2.90 0.01
M2 -1.68 3.27 0.01
M3 -1.92 3.72 0.01
RothC_0.6 M1 0.44 0.85 0.21
M2 0.21 0.48 0.37
M3 -0.08 0.24 0.49
RothC_p M1 -3.90 7.81 0.00
M2 -4.07 8.13 0.00
M3 -4.23 8.44 0.00
NPKS RothC_ M1 -8.48 16.67 0.09
M2 -8.69 17.04 0.09
M3 -8.93 17.48 0.09
RothC_0.6 M1 -5.71 11.21 0.13
M2 -5.94 11.60 0.12
M3 -6.22 12.12 0.12
RothC_p M1 -13.08 25.86 0.06
M2 -13.25 26.17 0.06
M3 -13.41 26.48 0.06

Table 6

Evaluation index of model performance of RothC_0.6 model for different model calibration methods of crop straw halving in Farm 850"

处理Treatment 模型Model 校准方法Calibration MD nRMSE(%) d
NPKS/2 RothC_0.6 M1 -0.73 1.42 0.62
M2 -0.96 1.81 0.54
M3 -1.24 2.34 0.45

Fig. 7

Comparison (a) and correlation (b) of simulated values and measured values with different calibration methods for crop straw halving in Farm 850"

[1]
中国科学院地理科学与资源研究所. 2020东北黑土地白皮书. 黑龙江: 中国科学院, 2021.
Institute of Geographic Sciences and Natural Resources Research. 2020 Northeast Black Soil White Paper. Heilongjiang: Chinese Academy of Sciences, 2021. (in Chinese)
[2]
汪景宽, 徐香茹, 裴久渤, 李双异. 东北黑土地区耕地质量现状与面临的机遇和挑战. 土壤通报, 2021, 52(3): 695-701.
WANG J K, XU X R, PEI J B, LI S Y. Current situations of black soil quality and facing opportunities and challenges in NorthEast China. Chinese Journal of Soil Science, 2021, 52(3): 695-701. (in Chinese)
[3]
张瑞, 杜国明, 张树文. 1986—2020年东北典型黑土区耕地资源时空变化及其驱动因素. 资源科学, 2023, 45(5): 939-950.

doi: 10.18402/resci.2023.05.05
ZHANG R, DU G M, ZHANG S W. Spatiotemporal changes and the driving factors of cultivated land resources of the typical black soil region in NorthEast China from 1986 to 2020. Resources Science, 2023, 45(5): 939-950. (in Chinese)

doi: 10.18402/resci.2023.05.05
[4]
祝贞科, 肖谋良, 魏亮, 王双, 丁济娜, 陈剑平, 葛体达. 稻田土壤固碳关键过程的生物地球化学机制及其碳中和对策. 中国生态农业学报(中英文), 2022, 30(4): 592-602.
ZHU Z K, XIAO M L, WEI L, WANG S, DING J N, CHEN J P, GE T D. Key biogeochemical processes of carbon sequestration in paddy soil and its countermeasures for carbon neutrality. Chinese Journal of Eco-Agriculture, 2022, 30(4): 592-602. (in Chinese)
[5]
LIU K L, HUANG J, LI D M, YU X C, YE H C, HU H W, HU Z H, HUANG Q H, ZHANG H M. Comparison of carbon sequestration efficiency in soil aggregates between upland and paddy soils in a red soil region of China. Journal of Integrative Agriculture, 2019, 18(6): 1348-1359.
[6]
LI C, FROLKING S, FROLKING T A. A model of nitrous oxide evolution from soil driven by rainfall events: 1. Model structure and sensitivity. Journal of Geophysical Research: Atmospheres, 1992, 97(D9): 9759-9776.
[7]
LI C S, FROLKING S, FROLKING T A. A model of nitrous oxide evolution from soil driven by rainfall events: 2. Model applications. Journal of Geophysical Research: Atmospheres, 1992, 97(D9): 9777-9783.
[8]
PARTON W J, SCHIMEL D S, COLE C V, OJIMA D S. Analysis of factors controlling soil organic matter levels in great Plains grasslands. Soil Science Society of America Journal, 1987, 51(5): 1173-1179.
[9]
JENKINSON D S, RAYNER J H. The turnover of soil organic matter in some of the rothamsted classical experiments. Soil Science, 1977, 123(5): 298-305.
[10]
WILLIAMS J R. The erosion-productivity impact calculator (EPIC) model: a case history. Philosophical Transactions of the Royal Society of London Series B: Biological Sciences, 1990, 329(1255): 421-428.
[11]
MCCOWN R L, HAMMER G L, HARGREAVES J N G, HOLZWORTH D P, FREEBAIRN D M. APSIM: A novel software system for model development, model testing and simulation in agricultural systems research. Agricultural Systems, 1996, 50(3): 255-271.
[12]
GARSIA A, MOINET A, VAZQUEZ C, CREAMER R E, MOINET G Y K. The challenge of selecting an appropriate soil organic carbon simulation model: A comprehensive global review and validation assessment. Global Change Biology, 2023, 29(20): 5760-5774.

doi: 10.1111/gcb.16896 pmid: 37571868
[13]
WANG S C, ZHAO Y W, WANG J Z, GAO J J, ZHU P, CUI X A, XU M G, ZHOU B K, LU C G. Estimation of soil organic carbon losses and counter approaches from organic materials in black soils of northeastern China. Journal of Soils and Sediments, 2020, 20(3): 1241-1252.
[14]
王文俊, 梁爱珍, 张延, 陈学文, 黄丹丹. 黑土区保护性耕作土壤有机碳动态的模型模拟研究. 中国农业科学, 2024, 57(10): 1943-1960. doi: 10.3864/j.issn.0578-1752.2024.10.008.
WANG W J, LIANG A Z, ZHANG Y, CHEN X W, HUANG D D. Model simulation research of soil organic carbon dynamics of long-term conservation tillage in black soil. Scientia Agricultura Sinica, 2024, 57(10): 1943-1960. doi: 10.3864/j.issn.0578-1752.2024. 10.008. (in Chinese)
[15]
SHIRATO Y, YOKOZAWA M. Applying the rothamsted carbon model for long-term experiments on Japanese paddy soils and modifying it by simple tuning of the decomposition rate. Soil Science and Plant Nutrition, 2005, 51(3): 405-415.
[16]
JIANG G Y, SHIRATO Y, XU M G, YAGASAKI Y, HUANG Q H, LI Z Z, NIE J, SHI X J. Testing the modified Rothamsted Carbon Model for paddy soils against the results from long-term experiments in Southern China. Soil Science and Plant Nutrition, 2013, 59(1): 16-26.
[17]
JORDON M W, SMITH P, LONG P R, BÜRKNER P C, PETROKOFSKY G, WILLIS K J. Can Regenerative Agriculture increase national soil carbon stocks? Simulated country-scale adoption of reduced tillage, cover cropping, and ley-arable integration using RothC. The Science of the Total Environment, 2022, 825: 153955.
[18]
WEIHERMÜLLER L, GRAF A, HERBST M, VEREECKEN H. Simple pedotransfer functions to initialize reactive carbon pools of the RothC model. European Journal of Soil Science, 2013, 64(5): 567-575.
[19]
HAO X X, WANG S Y, HAN X Z, GUO Z Y, LI L J. A dataset of topsoil nutrient content observed by Hailun Agroecosystem Experimental Station, CAS (2004-2015). China Scientific Data, 2020, 5(1): 1-14.
[20]
董桂军, 陈兴良, 于洪娇, 洪秀杰, 袁媛, 申贵男, 晏磊, 王彦杰, 王伟东. 寒区长期秸秆全量还田对水稻土理化特性的影响. 土壤与作物, 2019, 8(3): 251-257.
DONG G J, CHEN X L, YU H J, HONG X J, YUAN Y, SHEN G N, YAN L, WANG Y J, WANG W D. Effects of long-term all rice straw returning on soil physio-chemical properties in cold region. Soils and Crops, 2019, 8(3): 251-257. (in Chinese)
[21]
LIN Z Q, LU X Q, XU Y F, SUN W J, YU Y Q, ZHANG W, MISHRA U, KUZYAKOV Y, WANG G C, QIN Z C. Increased straw return promoted soil organic carbon accumulation in China’s croplands over the past 40 years. Science of the Total Environment, 2024, 945: 173903.
[22]
SIERRA C A, MÜLLER M, TRUMBORE S E. Models of soil organic matter decomposition: The SoilR package, version 1.0. Geoscientific Model Development, 2012, 5(4): 1045-1060.
[23]
R Core Team (2022). R: A language and environment for statisticalcomputing. R Foundation for Statistical Computing, Vienna, Austria.
[24]
ALLEN R G, PEREIRA L S, RAES D, SMITH M. Crop evapotranspiration guidelines for computing crop water requirements. FAO Irrigation and Drainage Paper 56, Rome, 1998.
[25]
ZHAO Y C, WANG M Y, HU S J, ZHANG X D, OUYANG Z, ZHANG G L, HUANG B, ZHAO S W, WU J S, XIE D T, ZHU B, YU D S, PAN X Z, XU S X, SHI X Z. Economics- and policy-driven organic carbon input enhancement dominates soil organic carbon accumulation in Chinese croplands. Proceedings of the National Academy of Sciences of the United States of America, 2018, 115(16): 4045-4050.
[26]
FALLOON P, SMITH P, COLEMAN K, MARSHALL S. Estimating the size of the inert organic matter pool from total soil organic carbon content for use in the Rothamsted carbon model. Soil Biology and Biochemistry, 1998, 30(8/9): 1207-1211.
[27]
姜桂英. 中国农田长期不同施肥的固碳潜力及预测[D]. 北京: 中国农业科学院, 2013.
JIANG G Y. Carbon sequestration potential and prediction of long-term different fertilization in farmland of China[D]. Beijing: Chinese Academy of Agricultural Sciences, 2013. (in Chinese)
[28]
YAO Z Y, ZHANG D B, LIU N, YAO P W, ZHAO N, LI Y Y, ZHANG S Q, ZHAI B N, HUANG D L, WANG Z H, CAO W D, ADL S, GAO Y J. Dynamics and sequestration potential of soil organic carbon and total nitrogen stocks of leguminous green manure-based cropping systems on the Loess Plateau of China. Soil and Tillage Research, 2019, 191: 108-116.
[29]
ROBERTSON F, NASH D. Limited potential for soil carbon accumulation using current cropping practices in Victoria, Australia. Agriculture, Ecosystems & Environment, 2013, 165: 130-140.
[30]
SHIRATO Y, YAGASAKI Y, NISHIDA M. Using different versions of the Rothamsted Carbon model to simulate soil carbon in long-term experimental plots subjected to paddy-upland rotation in Japan. Soil Science and Plant Nutrition, 2011, 57(4): 597-606.
[31]
WANG J, LU C, XU M, ZHU P, HUANG S, ZHANG W, PENG C, CHEN X, WU L. Soil organic carbon sequestration under different fertilizer regimes in north and NorthEast China: RothC simulation. Soil Use and Management, 2013, 29(2): 182-190.
[32]
杨阳, 王宝荣, 窦艳星, 薛志婧, 孙慧, 王云强, 梁超, 安韶山. 植物源和微生物源土壤有机碳转化与稳定研究进展. 应用生态学报, 2024, 35(1): 111-123.

doi: 10.13287/j.1001-9332.202401.011
YANG Y, WANG B R, DOU Y X, XUE Z J, SUN H, WANG Y Q, LIANG C, AN S S. Advances in the research of transformation and stabilization of soil organic carbon from plant and microbe. Chinese Journal of Applied Ecology, 2024, 35(1): 111-123. (in Chinese)

doi: 10.13287/j.1001-9332.202401.011
[33]
HUANG Y, YU Y Q, ZHANG W, SUN W J, LIU S L, JIANG J, WU J S, YU W T, WANG Y, YANG Z F. Agro-C: A biogeophysical model for simulating the carbon budget of agroecosystems. Agricultural and Forest Meteorology, 2009, 149(1): 106-129.
[34]
SHEN Q S, LIU X B, ZHANG X Y. Evaluating soil organic carbon changes after 16 years of soil relocation in Chinese Mollisols by optimizing the input data of the RothC model. Soil and Tillage Research, 2023, 225: 105561.
[35]
SKJEMSTAD J O, SPOUNCER L R, COWIE B, SWIFT R S. Calibration of the Rothamsted organic carbon turnover model (RothC ver. 26.3), using measurable soil organic carbon pools. Soil Research, 2004, 42(1): 79.
[36]
ZIMMERMANN M, LEIFELD A J, SCHMIDT A M W I, SMITH B P, FUHRER C J. Measured soil organic matter fractions can be related to pools in the RothC model. European Journal of Soil Science, 2007, 58(3): 658-667.
[37]
XU X L, LIU W, KIELY G. Modeling the change in soil organic carbon of grassland in response to climate change: Effects of measured versus modelled carbon pools for initializing the Rothamsted Carbon model. Agriculture, Ecosystems & Environment, 2011, 140(3/4): 372-381.
[38]
HERBST M, WELP G, MACDONALD A, JATE M, HÄDICKE A, SCHERER H, GAISER T, HERRMANN F, AMELUNG W, VANDERBORGHT J. Correspondence of measured soil carbon fractions and RothC pools for equilibrium and non-equilibrium states. Geoderma, 2018, 314: 37-46.
[39]
梁超, 朱雪峰. 土壤微生物碳泵储碳机制概论. 中国科学: 地球科学, 2021, 51(5): 680-695.
LIANG C, ZHU X F. The soil microbial carbon pump as a new concept for terrestrial carbon sequestration. Scientia Sinica (Terrae), 2021, 51(5): 680-695. (in Chinese)
[40]
朱雪峰, 孔维栋, 黄懿梅, 肖可青, 罗煜, 安韶山, 梁超. 土壤微生物碳泵概念体系2.0. 应用生态学报, 2024, 35(1): 102-110.

doi: 10.13287/j.1001-9332.202401.018
ZHU X F, KONG W D, HUANG Y M, XIAO K Q, LUO Y, AN S S, LIANG C. Soil microbial carbon pump conceptual framework 2.0. Chinese Journal of Applied Ecology, 2024, 35(1): 102-110. (in Chinese)

doi: 10.13287/j.1001-9332.202401.018
[1] SHI Fan, LI WenGuang, YI ShuSheng, YANG Na, CHEN YuMeng, ZHENG Wei, ZHANG XueChen, LI ZiYan, ZHAI BingNian. The Variation Characteristics of Soil Organic Carbon Fractions Under the Combined Application of Organic and Inorganic Fertilizers [J]. Scientia Agricultura Sinica, 2025, 58(4): 719-732.
[2] MU ShuJia, DONG LiXia, LI Guang, YAN ZhenGang, LU YuLan. Optimization of N2O Emission Parameters in Dryland Spring Wheat Farmland Soil Based on Whale Optimization Algorithm [J]. Scientia Agricultura Sinica, 2025, 58(3): 537-547.
[3] ZHOU GuangFei, MA Liang, MA Lu, ZHANG ShuYu, ZHANG HuiMin, SONG XuDong, ZHANG ZhenLiang, LU HuHua, HAO DeRong, MAO YuXiang, XUE Lin, CHEN GuoQing. Genome-Wide Association Study of Husk Traits in Maize [J]. Scientia Agricultura Sinica, 2025, 58(3): 431-442.
[4] ZHANG FangFang, SONG QiLong, GAO Na, BAI Ju, LI Yang, YUE ShanChao, LI ShiQing. Effects of Long-Term Mulching Practices on Maize Yield, Soil Organic Carbon and Nitrogen Fractions and Indexes Related to Carbon and Nitrogen Pool on the Loess Plateau [J]. Scientia Agricultura Sinica, 2025, 58(3): 507-519.
[5] WANG RongRong, XU NingLu, HUANG XiuLi, ZHAO KaiNan, HUANG Ming, WANG HeZheng, FU GuoZhan, WU JinZhi, LI YouJun. Effects of One-Off Irrigation and Nitrogen Fertilizer Management on Grain Yield and Quality in Dryland Wheat [J]. Scientia Agricultura Sinica, 2025, 58(1): 43-57.
[6] ZHANG Ying, SHI TingRui, CAO Rui, PAN WenQiu, SONG WeiNing, WANG Li, NIE XiaoJun. Genome-Wide Association Study of Drought Tolerance at Seedling Stage in ICARDA-Introduced Wheat [J]. Scientia Agricultura Sinica, 2024, 57(9): 1658-1673.
[7] GAO YaFei, ZHAO YuanBo, XU Lin, SUN JiaYue, XIA YuXuan, XUE Dan, WU HaiWen, NING Hang, WU AnChi, WU Lin. Long-Term Sphagnum Cultivation in Cold Waterlogged Paddy Fields Increases Organic Carbon Content and Decreases Soil Extracellular Enzyme Activities [J]. Scientia Agricultura Sinica, 2024, 57(8): 1533-1546.
[8] ZHAO ZhenJian, WANG Kai, CHEN Dong, SHEN Qi, YU Yang, CUI ShengDi, WANG JunGe, CHEN ZiYang, YU ShiXin, CHEN JiaMiao, WANG XiangFeng, TANG GuoQing. Integrated Aanalysis of Genome and DNA Methylation for Screening Key Genes Related to Pork Quality Traits [J]. Scientia Agricultura Sinica, 2024, 57(7): 1394-1406.
[9] SONG YaRong, CHANG DanNa, ZHOU GuoPeng, GAO SongJuan, DUAN TingYu, CAO WeiDong. Effect and Mechanism of Phosphate-Solubilizing Bacterial on Activating of Low-Grade Phosphate Rock Powder in Red Paddy Soil [J]. Scientia Agricultura Sinica, 2024, 57(6): 1102-1116.
[10] DANG JianYou, JIANG WenChao, SUN Rui, SHANG BaoHua, PEI XueXia. Response of Wheat Grain Yield and Water Use Efficiency to Ploughing Time and Precipitation and Its Distribution in Dryland [J]. Scientia Agricultura Sinica, 2024, 57(6): 1049-1065.
[11] WEI NaiCui, TAO JinBo, YUAN MingYang, ZHANG Yu, KAI MengXiang, QIAO Ling, WU BangBang, HAO YuQiong, ZHENG XingWei, WANG JuanLing, ZHAO JiaJia, ZHENG Jun. Seedling Characterization and Genetic Analysis of Low Phosphorus Tolerance in Shanxi Varieties [J]. Scientia Agricultura Sinica, 2024, 57(5): 831-845.
[12] GAO ShangJie, LIU XingRen, LI YingChun, LIU XiaoWan. Effects of Biochar and Straw Return on Greenhouse Gas Emissions and Global Warming Potential in the Farmland [J]. Scientia Agricultura Sinica, 2024, 57(5): 935-949.
[13] LI FaJi, CHENG DunGong, YU XiaoCong, WEN WeiE, LIU JinDong, ZHAI ShengNan, LIU AiFeng, GUO Jun, CAO XinYou, LIU Cheng, SONG JianMin, LIU JianJun, LI HaoSheng. Genome-Wide Association Studies for Canopy Activity Related Traits and Its Genetic Effects on Yield-Related Traits [J]. Scientia Agricultura Sinica, 2024, 57(4): 627-637.
[14] QIU HaiHua, KUAI LeiXin, ZHANG Lu, LIU LiSheng, WEN ShiLin, CAI ZeJiang. Characteristics of Acidity and Nutrient Changes in Red Soil After Conversion of Paddy Field to Dry Land and Vegetable Field [J]. Scientia Agricultura Sinica, 2024, 57(3): 525-538.
[15] SUN Yue, REN KeYu, ZOU HongQin, GAO HongJun, ZHANG ShuiQing, LI DeJin, LI BingJie, LIAO ChuQian, DUAN YingHua, XU MingGang. Effect of Long-Term Straw Returning on the Soil Organic Carbon Bound to Iron Oxides in Black Soil and Fluvo-Aquic Soil [J]. Scientia Agricultura Sinica, 2024, 57(19): 3823-3834.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
京ICP备09089781号-30
Copyright 2018 © Editorial office of Scientia Agricultura Sinica
Tel: 86-010-82106279    E-mail: zgnykx@mail.caas.net.cn
Supported by Beijing Magtech Co.Ltd