农业生态环境-氮素合辑Agro-ecosystem & Environment—Nitrogen
|Statistical analysis of nitrogen use efficiency in Northeast China using multiple linear regression and random forest
|LIU Ying-xia1, 2, 3, Gerard B. M. HEUVELINK2, 3, Zhanguo BAI3, HE Ping1, JIANG Rong1, HUANG Shao-hui1, XU Xin-peng1
1 Key Laboratory of Plant Nutrition and Fertilizer, Ministry of Agriculture and Rural Affairs/Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, P.R.China
2 Soil Geography and Landscape Group, Wageningen University, Wageningen 6700 AA, The Netherlands
3 International Soil Reference and Information Centre - World Soil Information, Wageningen 6700 AJ, The Netherlands
Understanding the spatial-temporal dynamics of crop nitrogen (N) use efficiency (NUE) and the relationship with explanatory environmental variables can support land-use management and policymaking. Nevertheless, the application of statistical models for evaluating the explanatory variables of space-time variation in crop NUE is still under-researched. In this study, stepwise multiple linear regression (SMLR) and Random Forest (RF) were used to evaluate the spatial and temporal variation of NUE indicators (i.e., partial factor productivity of N (PFPN); partial nutrient balance of N (PNBN)) at county scale in Northeast China (Heilongjiang, Liaoning and Jilin provinces) from 1990 to 2015. Explanatory variables included agricultural management practices, topography, climate, economy, soil and crop types. Results revealed that the PFPN was higher in the northern parts and lower in the center of the Northeast China and PNBN increased from southern to northern parts during the 1990–2015 period. The NUE indicators decreased with time in most counties during the study period. The model efficiency coefficients of the SMLR and RF models were 0.44 and 0.84 for PFPN, and 0.67 and 0.89 for PNBN, respectively. The RF model had higher relative importance of soil and climatic covariates and lower relative importance of crop covariates compared to the SMLR model. The planting area index of vegetables and beans, soil clay content, saturated water content, enhanced vegetation index in November & December, soil bulk density, and annual minimum temperature were the main explanatory variables for both NUE indicators. This is the first study to show the quantitative relative importance of explanatory variables for NUE at a county level in Northeast China using RF and SMLR. This novel study gives reference measurements to improve crop NUE which is one of the most effective means of managing N for sustainable development, ensuring food security, alleviating environmental degradation and increasing farmer’s profitability.
Received: 01 September 2021
Accepted: 03 December 2021
We are specially acknowledgeable financial support from the China Scholarship Council (CSC) (201903250115). This research was supported by the National Natural Science Foundation of China (31972515) and the China Agriculture Research System of MOF and MARA (CARS–09-P31).
|About author: LIU Ying-xia, E-mail: firstname.lastname@example.org; Correspondence HE Ping, Tel: +86-10-82105638, E-mail: email@example.com
Cite this article:
LIU Ying-xia, Gerard B. M. HEUVELINK, Zhanguo BAI, HE Ping, JIANG Rong, HUANG Shao-hui, XU Xin-peng.
Statistical analysis of nitrogen use efficiency in Northeast China using multiple linear regression and random forest. Journal of Integrative Agriculture, 21(12): 3637-3657.
| Aghdaei N, Kokogiannakis G, Daly D, McCarthy T. 2017. Linear regression models for prediction of annual heating and cooling demand in representative Australian residential dwellings. Energy Procedia, 121, 79–86.
Allys E, Marchand T, Cardoso J F, Villaescusa-Navarro F, Ho S, Mallat S. 2020. New interpretable statistics for large-scale structure analysis and generation. Physical Review D, 102, 103506.
de Beaufort H W L, Nauta F J H, Conti M, Cellitti E, Trentin C, Faggiano E, van Bogerijen G H W, Figueroa C A, Moll F L, van Herwaarden J A, Auricchio F, Auricchio F. 2017. Extensibility and distensibility of the thoracic aorta in patients with aneurysm. European Journal of Vascular and Endovascular Surgery, 53, 199–205.
Breiman L. 2001. Random forests. Machine Learning, 45, 5–32.
Brentrup F, Pallière C. 2010. Nitrogen use efficiency as an agro-environmental indicator. In: Proceedings of the OECD Workshop on Agri-Environmental Indicators. Leysin, Switzerland.
Cen H, Wan L, Zhu J, Li Y, Li X, Zhu Y, Weng H, Wu W, Yin W, Xu C, Bao Y, Feng L, Shou J, He Y. 2019. Dynamic monitoring of biomass of rice under different nitrogen treatments using a lightweight UAV with dual image-frame snapshot cameras. Plant Methods, 15, 32.
Chen C, Lei C, Deng A, Qian C, Hoogmoed W, Zhang W. 2011. Will higher minimum temperatures increase corn production in Northeast China? An analysis of historical data over 1965–2008. Agricultural and Forest Meteorology, 151, 1580–1588.
Chien S H, Prochnow L I, Cantarella A H. 2009. Recent developments of fertilizer production and use to improve nutrient efficiency and minimize environmental impacts. Advances in Agronomy, 102, 267–322.
Climatic Research Unit. 2015. University of East Anglia, Norwich, UK. 1978–2015. [2018-02-19]. http://www.cru.uea.ac.uk/data
Diacono M, Rubino P, Montemurro F. 2013. Precision nitrogen management of wheat. A review. Agronomy for Sustainable Development, 33, 219–241.
Ding W, Xu X, He P, Ullah S, Zhang J, Cui Z, Zhou W. 2018. Improving yield and nitrogen use efficiency through alternative fertilization options for rice in China: A meta-analysis. Field Crops Research, 227, 11–18.
Dobermann A. 2007. Nutrient use efficiency measurement and management. In: Krauss K I, Heffer P, eds., Fertilizer Best Management Practice, General Principles, Strategy for their Adoption and Voluntary Initiatives vs. Regulations. International Fertilizer Association, Paris. pp. 1–28.
Dobermann A, Witt C, Abdulrachman S, Gines H C, Nagarajan R, Son T T, Tan P S, Wang G H, Chien N V, Thoa V T K, Phung C V, Stalin P, Muthukrishnan P, Ravi V, Babu M, Simbahan G C, Adviento M A A, Phung C V. 2003. Estimating indigenous nutrient supplies for site-specific nutrient management in irrigated rice. Agronomy Journal, 95, 924–935.
ESA (European Space Agency). 2021. ESA/CCI viewer. [2021-01-24]. http://maps.elie.ucl.ac.be/CCI/viewer/download.php
EU Nitrogen Expert Panel. 2015. Nitrogen Use Efficiency (NUE) - an Indicator for the Utilization of Nitrogen in Agriculture and Food Systems. Wageningen University, Wageningen, Netherlands.
Everingham Y, Sexton J, Skocaj D, Inman-Bamber G. 2016. Accurate prediction of sugarcane yield using a random forest algorithm. Agronomy for Sustainable Development, 36, 27.
Fageria N K, Baligar V C. 2005. Enhancing nitrogen use efficiency in crop plants. Advances in Agronomy, 88, 97–185.
Fan X, Xu D, Wang Y, Zhang X, Cao S, Mou S, Ye N. 2014. The effect of nutrient concentrations, nutrient ratios and temperature on photosynthesis and nutrient uptake by Ulva prolifera: Implications for the explosion in green tides. Journal of Applied Phycology, 26, 537–544.
Fixen P, Brentrup F, Bruulsema T, Garcia F, Norton R, Zingore S. 2015. Nutrient/fertilizer use efficiency: measurement, current situation and trends. In: Managing Water and Fertilizer for Sustainable Agricultural Intensification. IFA, IWMI, IPNI, IPI. Paris, France. pp. 8–37.
Fraiwan L, Lweesy K, Khasawneh N, Wenz H, Dickhaus H. 2012. Automated sleep stage identification system based on time-frequency analysis of a single EEG channel and random forest classifier. Computer Methods and Programs in Biomedicine, 108, 10–19.
Galloway J N, Townsend A R, Erisman J W, Bekunda M, Cai Z, Freney J R, Martinelli L A, Seitzinger S P, Sutton M A. 2008. Transformation of the nitrogen cycle: Recent trends, questions, and potential solutions. Science, 320, 889–892.
Gislason P O, Benediktsson J A, Sveinsson J R. 2006. Random forests for land cover classification. Pattern Recognition Letters, 27, 294–300.
Grömping U. 2006. Relative importance for linear regression in R: the package relaimpo. Journal of Statistical Software, 17, 1–27.
Grömping U. 2009. Variable importance assessment in regression: Linear regression versus Random Forest. The American Statistician, 63, 308–319.
Gurung R B, Breidt F J, Dutin A, Ogle S M. 2009. Predicting enhanced vegetation index (EVI) curves for ecosystem modeling applications. Remote Sensing of Environment, 113, 2186–2193.
Hamoud Y A, Shaghaleh H, Sheteiwy M, Guo X, Elshaikh N A, Khan N U, Oumarou A, Rahim S F. 2019. Impact of alternative wetting and soil drying and soil clay content on the morphological and physiological traits of rice roots and their relationships to yield and nutrient use-efficiency. Agricultural Water Management, 223, 105706.
Harris I, Jones P D, Osborn T J, Lister D H. 2014. Updated high-resolution grids of monthly climatic observations - the CRU TS3.10 Dataset. International Journal of Climatology, 34, 623–642.
He P, Li S, Jin J, Wang H, Li C, Wang Y, Cui R. 2009. Performance of an optimized nutrient management system for double-cropped wheat–maize rotations in North-Central China. Agronomy Journal, 101, 1489–1496.
He W, Jiang R, He P, Yang J, Zhou W, Ma J, Liu Y. 2018. Estimating soil nitrogen balance at regional scale in China’s croplands from 1984 to 2014. Agricultural Systems, 167, 125–135.
Hengl T, Heuvelink G B M, Kempen B, Leenaars J G B, Walsh M G, Shepherd K D, Sila A, MacMillan R A, de Jesus J M, Tamene L, Tondoh J E. 2015. Mapping soil properties of Africa at 250 m resolution: Random forests significantly improve current predictions. PLoS ONE, 10, e0125814.
Hengl T, de Jesus J M, Heuvelink G B M, Gonzalez M R, Kilibarda M, Blagotić A, Shangguan W, Wright M N, Geng X Y, Bauer-Marschallinger B, Guevara M A, Vargas R, Macmillan R A, Batjes N H, Leenaars J G B, Ribeiro E, Wheeler I, Mantel S, Kempen B. 2017. SoilGrids250m: Global gridded soil information based on machine learning. PLoS ONE, 12, e0169748.
Ichami S M, Shepherd K D, Sila A M, Stoorvogel J J, Hoffland E. 2019. Fertilizer response and nitrogen use efficiency in African smallholder maize farms. Nutrient Cycling in Agroecosystems, 113, 1–19.
Iqbal A, He L, Khan A, Wei S, Akhtar K, Ali I, Ullah S, Munsif F, Zhao Q, Jiang L. 2019. Organic manure coupled with inorganic fertilizer: An approach for the sustainable production of rice by improving soil properties and nitrogen use efficiency. Agronomy, 9, 651.
Ishaq M, Ibrahim M, Lal R. 2002. Tillage effects on soil properties at different levels of fertilizer application in Punjab, Pakistan. Soil and Tillage Research, 68, 93–99.
Jin D, Gao J, Jiang P, Lv X, Wang Y, Zhang W. 2017. Nitrogen use efficiency and rice yield of different locations in Northeast China. National Academy Science Letters, 40, 227–232.
Johnston A M, Bruulsema T W. 2014. 4R nutrient stewardship for improved nutrient use efficiency. Procedia Engineering, 83, 365–370.
Kabacoff R I. 2011. R in Action: Data Analysis and Graphics with R. Manning, Shelter Island.
Kutner M H, Nachtsheim C J, Neter J. 2004. Applied Linear Regression Models. 4th ed. McGraw-Hill Irwin, USA. pp. 563–568.
Lassaletta L, Billen G, Grizzetti B, Anglade J, Garnier J. 2014. 50 year trends in nitrogen use efficiency of world cropping systems: the relationship between yield and nitrogen input to cropland. Environmental Research Letters, 9, 105011.
Li B, Bi Z, Xiong Z. 2017. Dynamic responses of nitrous oxide emission and nitrogen use efficiency to nitrogen and biochar amendment in an intensified vegetable field in southeastern China. GCB Bioenergy, 9, 400–413.
Li C, Hoffland E, Kuyper T W, Yu Y, Zhang C, Li H, Zhang F, van der Werf W. 2020. Syndromes of production in intercropping impact yield gains. Nature Plants, 6, 653–660.
Liang S, Li Y, Zhang X, Sun Z, Sun N, Duan Y, Xu M, Wu L. 2018. Response of crop yield and nitrogen use efficiency for wheat–maize cropping system to future climate change in northern China. Agricultural and Forest Meteorology, 262, 310–321.
Lin L. 1989. A concordance correlation coefficient to evaluate reproducibility. Biometrics, 45, 255–268.
Lindeman R H, Merenda P F, Gold R Z. 1980. Introduction to Bivariate and Multivariate Analysis. Scott, Foresman and Company, Glenview, IL, USA.
Liu M, Liu X, Liu D, Ding C, Jiang J. 2015. Multivariable integration method for estimating sea surface salinity in coastal waters from in situ data and remotely sensed data using random forest algorithm. Computers & Geosciences, 75, 44–56.
Liu Y, Gao M, Wu W, Tanveer S K, Wen X, Liao Y. 2013. The effects of conservation tillage practices on the soil water-holding capacity of a non-irrigated apple orchard in the Loess Plateau, China. Soil and Tillage Research, 130, 7–12.
Liu Y, Heuvelink G B M, Bai Z, He P, Xu X, Ma J, Masiliūnas D. 2020. Space-time statistical analysis and modelling of nitrogen use efficiency indicators at provincial scale in China. European Journal of Agronomy, 115, 126032.
Lu C, Zhang J, Cao P, Hatfield J L. 2019. Are we getting better in using nitrogen?: Variations in nitrogen use efficiency of two cereal crops across the United States. Earth’s Future, 7, 939–952.
Meyer H, Pebesma E. 2021. Predicting into unknown space? Estimating the area of applicability of spatial prediction models. Methods in Ecology and Evolution, 12, 1620–1633.
Miao Y, Stewart B A, Zhang F. 2011. Long-term experiments for sustainable nutrient management in China. A review. Agronomy for Sustainable Development, 31, 397–414.
Mogollón J M, Lassaletta L, Beusen A H W, van Grinsven H J M, Westhoek H, Bouwman A F. 2018. Assessing future reactive nitrogen inputs into global croplands based on the shared socioeconomic pathways. Environmental Research Letters, 13, 044008.
Mukhopadhyay S, Masto R E, Tripathi R C, Srivastava N K. 2019. Application of soil quality indicators for the phytorestoration of mine spoil dumps. In: Phytomanagement of Polluted Sites. Elsevier, San Diego. pp. 361–388.
Nicodemus K K, Malley J D, Strobl C, Ziegler A. 2010. The behaviour of random forest permutation-based variable importance measures under predictor correlation. BMC Bioinformatics, 11, 1–13.
Omara P, Aula L, Oyebiyi F, Raun W R. 2019. World cereal nitrogen use efficiency trends: Review and current knowledge. Agrosystems, Geosciences & Environment, 2, 1–8.
Pan X, Baquy M A A, Guan P, Yan J, Wang R, Xu R, Xie L. 2020. Effect of soil acidification on the growth and nitrogen use efficiency of maize in Ultisols. Journal of Soils and Sediments, 20, 1435–1445.
Peng S, Huang J, Sheehy J E, Laza R C, Visperas R M, Zhong X, Centeno G S, Khush G S, Cassman K G. 2004. Rice yields decline with higher night temperature from global warming. Proceedings of the National Academy of Sciences of the United States of America, 101, 9971–9975.
Peng X, Maharjan B, Yu C, Su A, Jin V, Ferguson R B. 2015. A laboratory evaluation of ammonia volatilization and nitrate leaching following nitrogen fertilizer application on a coarse-textured soil. Agronomy Journal, 107, 871–879.
Peters J, Verhoest N E C, Samson R, Boeckx P, De Baets B. 2008. Wetland vegetation distribution modelling for the identification of constraining environmental variables. Landscape Ecology, 23, 1049–1065.
Prasad A M, Iverson L R, Liaw A. 2006. Newer classification and regression tree techniques: bagging and random forest for ecological prediction. Ecosystems, 9, 181–199.
Quan Z, Zhang X, Fang Y, Davidson E A. 2021. Different quantification approaches for nitrogen use efficiency lead to divergent estimates with varying advantages. Nature Food, 2, 241–245.
R Core Team. 2021. R: A Language and environment for statistical computing. R foundation for statistical computing, Vienna, Austria. [2021-01-19]. http://www.r-project.org/
Ramírez E, Reheul D. 2009. Statistical modelling of nitrogen use efficiency of dairy farms in Flanders. Agronomy for Sustainable Development, 29, 339–352.
Ray D K, Gerber J S, MacDonald G K, West P C. 2015. Climate variation explains a third of global crop yield variability. Nature Communications, 6, 1–9.
Ren C, Wang Z, Song K, Zhang B, Liu D, Yang G, Liu Z. 2011. Spatial variation of soil organic carbon and its relationship with environmental factors in the farming-pastoral ecotone of Northeast China. Fresenius Environmental Bulletin, 20, 253–261.
De Reu J, Bourgeois J, Bats M, Zwertvaegher A, Gelorini V, De Smedt P, Chu W, Antrop M, De Maeyer P, Finke P, van Meirvenne M, Verniers J, Crombe P. 2013. Application of the topographic position index to heterogeneous landscapes. Geomorphology, 186, 39–49.
Richardson H J, Hill D J, Denesiuk D R, Fraser L H. 2017. A comparison of geographic datasets and field measurements to model soil carbon using random forests and stepwise regressions (British Columbia, Canada). GIScience & Remote Sensing, 54, 573–591.
Sakamoto T. 2020. Incorporating environmental variables into a MODIS-based crop yield estimation method for United States corn and soybeans through the use of a random forest regression algorithm. ISPRS Journal of Photogrammetry and Remote Sensing, 160, 208–228.
Shangguan W, Dai Y, Duan Q, Liu B, Yuan H. 2014. A global soil data set for earth system modeling. Journal of Advances in Modeling Earth Systems, 6, 249–263.
Singh G, Williard K, Schoonover J, Nelson K A, Kaur G. 2019. Cover crops and landscape position effects on nitrogen dynamics in plant–soil–water pools. Water, 11, 513.
Snyder C S, Bruulsema T W. 2007. Nutrient Use Efficiency and Effectiveness in North America: Indices of Agronomic and Environmental Benefit. Publication International Plant Nutrition Institute, USA.
de Sousa L M, Poggio L, Batjes N H, Heuvelink G, Kempen B, Riberio E, Rossiter D. 2020. SoilGrids 2.0: Producing quality-assessed soil information for the globe. Soil Discussions, 7, 217–240.
Strobl C, Malley J, Tutz G. 2009. An introduction to recursive partitioning: Rationale, application, and characteristics of classification and regression trees, bagging, and random forests. Psychological Methods, 14, 323.
Tang Z, Ma J, Peng H. Wang S, Wei J. 2017. Spatiotemporal changes of vegetation and their responses to temperature and precipitation in upper Shiyang river basin. Advances in Space Research, 60, 969–979.
Ullah H, Santiago-Arenas R, Ferdous Z, Attia A, Datta A. 2019. Improving water use efficiency, nitrogen use efficiency, and radiation use efficiency in field crops under drought stress: A review. Advances in Agronomy, 156, 109–157.
Wright M N, Ziegler A. 2017. Ranger: A fast implementation of random forests for high dimensional data in C++ and R. Journal of Statistical Software, 77, 1–17.
Xu X, He P, Pampolino M F, Chuan L, Johnston A M, Qiu S, Zhao S, Zhou W. 2013. Nutrient requirements for maize in China based on QUEFTS analysis. Field Crops Research, 150, 115–125.
Xu X, He P, Pampolino M F, Li Y, Liu S, Xie J, Hou Y, Zhou W. 2016. Narrowing yield gaps and increasing nutrient use efficiencies using the Nutrient Expert system for maize in Northeast China. Field Crops Research, 194, 75–82.
Xu X, He P, Qiu S, Pampolino M F, Zhao S, Johnston A M, Zhou W. 2014. Estimating a new approach of fertilizer recommendation across small-holder farms in China. Field Crops Research, 163, 10–17.
Yousaf M, Li X, Zhang Z, Ren T, Cong R, Ata-Ul-Karim S T, Fahad S, Shah A N, Lu J. 2016. Nitrogen fertilizer management for enhancing crop productivity and nitrogen use efficiency in a rice–oilseed rape rotation system in China. Frontiers in Plant Science, 7, 1496.
Zhang X, Davidson E A, Mauzerall D L, Searchinger T D, Dumas P, Shen Y. 2015. Managing nitrogen for sustainable development. Nature, 528, 51–59.
Zhu Q, de Vries W, Liu X, Hao T, Zeng M, Shen J, Zhang F. 2018. Enhanced acidification in Chinese croplands as derived from element budgets in the period 1980–2010. Science of the Total Environment, 618, 1497–1505.
Zscheischler J, Orth R, Seneviratne S I. 2017. Bivariate return periods of temperature and precipitation explain a large fraction of European crop yields. Biogeosciences, 14, 3309–3320.
|No Suggested Reading articles found!