Methodological considerations for redesigning sustainable cropping systems: the value of data-mining large and detailed farm data sets at the cropping system level

doi:10.15302/J-FASE-2019292

Front. Agr. Sci. Eng.

2020, Vol. 7

Issue (1) : 21-27 https://doi.org/10.15302/J-FASE-2019292

RESEARCH ARTICLE

Methodological considerations for redesigning sustainable cropping systems: the value of data-mining large and detailed farm data sets at the cropping system level

Nicolas MUNIER-JOLAIN¹(

), Martin LECHENET^1,²

¹. Agroecology Research Unit, AgroSup Dijon, INRAE. Univ. Bourgogne, Univ. Bourgogne Franche-Comté, F-21000 Dijon, France
². Dijon Céréales, F-21600 Longvic, France

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Abstract

Redesigning cropping and farming systems to enhance their sustainability is mainly addressed in scientific studies using experimental and modeling approaches. Large data sets collected from real farms allow for the development of innovative methods to produce generic knowledge. Data mining methods allow for the diversity of systems to be considered holistically and can take into account the diversity of production contexts to produce site-specific results. Based on the very few known studies using such methods to analyze the crop management strategies affecting pesticide use and their effect on farm performance, we advocate further investment in the development of large data sets that can support future research programs on farming system design.

Keywords data mining holistic Integrated Pest Management economics DEPHY network

Corresponding Author(s): Nicolas MUNIER-JOLAIN

Just Accepted Date: 19 November 2019 Online First Date: 16 December 2019 Issue Date: 02 March 2020

Cite this article:

Nicolas MUNIER-JOLAIN,Martin LECHENET. Methodological considerations for redesigning sustainable cropping systems: the value of data-mining large and detailed farm data sets at the cropping system level[J]. Front. Agr. Sci. Eng. , 2020, 7(1): 21-27.

URL:

https://academic.hep.com.cn/fase/EN/10.15302/J-FASE-2019292
https://academic.hep.com.cn/fase/EN/Y2020/V7/I1/21

1	N Alexandratos, J Bruinsma. World agriculture towards 2030/2050: the 2012 revision. Rome: FAO, 2012
2	P L G Vlek, G Rodríguez-Kuhl, R Sommer. Energy use and CO2 production in tropical agriculture and means and strategies for reduction or mitigation. Environment, Development and Sustainability, 2004, 6(1–2): 213–233 https://doi.org/10.1023/B:ENVI.0000003638.42750.36
3	V Deytieux, T Nemecek, R Freiermuth Knuchel, G Gaillard, N Munier-Jolain. Is Integrated Weed Management efficient for reducing environmental impacts of cropping systems? A case study based on life cycle assessment. European Journal of Agronomy, 2012, 36(1): 55–65 https://doi.org/10.1016/j.eja.2011.08.004
4	J J Schröder, A L Smit, D Cordell, A Rosemarin. Improved phosphorus use efficiency in agriculture: a key requirement for its sustainable use. Chemosphere, 2011, 84(6): 822–831 https://doi.org/10.1016/j.chemosphere.2011.01.065 pmid: 21349568
5	D Pimentel, J Houser, E Preiss, O White, H Fang, L Mesnick, T Barsky, S Tariche, J Schreck, S Alpert. Water resources: agriculture, the environment, and society. Bioscience, 1997, 47(2): 97–106 https://doi.org/10.2307/1313020
6	G P Robertson, E A Paul, R R Harwood. Greenhouse gases in intensive agriculture: contributions of individual gases to the radiative forcing of the atmosphere. Science, 2000, 289(5486): 1922–1925 https://doi.org/10.1126/science.289.5486.1922 pmid: 10988070
7	R Lal. Soil carbon sequestration impacts on global climate change and food security. Science, 2004, 304(5677): 1623–1627 https://doi.org/10.1126/science.1097396 pmid: 15192216
8	E Silva, M A Daam, M J Cerejeira. Aquatic risk assessment of priority and other river basin specific pesticides in surface waters of Mediterranean river basins. Chemosphere, 2015, 135: 394–402 https://doi.org/10.1016/j.chemosphere.2015.05.013 pmid: 26002046
9	J Wang, Z Fu, H Qiao, F Liu. Assessment of eutrophication and water quality in the estuarine area of Lake Wuli, Lake Taihu, China. Science of the Total Environment, 2019, 650(Pt 1): 1392–1402 https://doi.org/10.1016/j.scitotenv.2018.09.137 pmid: 30308826
10	H Hamsan, Y B Ho, S Z Zaidon, Z Hashim, N Saari, A Karami. Occurrence of commonly used pesticides in personal air samples and their associated health risk among paddy farmers. Science of the Total Environment, 2017, 603–604: 381–389 https://doi.org/10.1016/j.scitotenv.2017.06.096 pmid: 28633115
11	F J J A Bianchi, C J H Booij, T Tscharntke. Sustainable pest regulation in agricultural landscapes: a review on landscape composition, biodiversity and natural pest control. Proceedings. Biological Sciences, 2006, 273(1595): 1715–1727 https://doi.org/10.1098/rspb.2006.3530 pmid: 16790403
12	G Martel, S Aviron, A Joannon, E Lalechère, B Roche, H Boussard. Impact of farming systems on agricultural landscapes and biodiversity: from plot to farm and landscape scales. European Journal of Agronomy, 2019, 107: 53–62 https://doi.org/10.1016/j.eja.2017.07.014
13	F Gould, Z S Brown, J Kuzma. Wicked evolution: can we address the sociobiological dilemma of pesticide resistance? Science, 2018, 360(6390): 728–732 https://doi.org/10.1126/science.aar3780 pmid: 29773742
14	X Poux, P M Aubert. An agroecological Europe in 2050: multifunctional agriculture for healthy eating—findings from the Ten Years For Agroecology (TYFA) modelling exercise. Paris: IDDRI, 2018
15	M Lechenet, D Makowski, G Py, N Munier-Jolain. Profiling farming management strategies with contrasting pesticide use in France. Agricultural Systems, 2016, 149: 40–53 https://doi.org/10.1016/j.agsy.2016.08.005
16	J Bürger, F de Mol, B Gerowitt. Influence of cropping system factors on pesticide use intensity—a multivariate analysis of on-farm data in North East Germany. European Journal of Agronomy, 2012, 40: 54–63 https://doi.org/10.1016/j.eja.2012.02.008
17	M Lechenet, F Dessaint, G Py, D Makowski, N Munier-Jolain. Reducing pesticide use while preserving crop productivity and profitability on arable farms. Nature Plants, 2017, 3: 17008, https://doi.org/10.1038/nplants.2017.8
18	J N Aubertot, M H Robin. Injury Profile SIMulator, a qualitative aggregative modelling framework to predict crop injury profile as a function of cropping practices, and the abiotic and biotic environment. I. Conceptual bases. PLoS One, 2013, 8(9): e73202 https://doi.org/10.1371/journal.pone.0073202 pmid: 24019908
19	D Lu, F Lu, J Pan, Z Cui, C Zou, X Chen, M He, Z Wang. The effects of cultivar and nitrogen management on wheat yield and nitrogen use efficiency in the North China Plain. Field Crops Research, 2015, 171: 157–164 https://doi.org/10.1016/j.fcr.2014.10.012
20	B D Soane, B C Ball. Review of management and conduct of long-term tillage studies with special reference to a 25-yr experiment on barley in Scotland. Soil & Tillage Research, 1998, 45(1–2): 17–37 https://doi.org/10.1016/S0167-1987(97)00070-6
21	D D B Richter, M Hofmockel, M A Callaham, D S Powlson, P Smith. Long-term soil experiments: keys to managing Earth’s rapidly chancing ecosystems. Soil Science Society of America Journal, 2007, 71(2): 266–279 https://doi.org/10.2136/sssaj2006.0181
22	A Edward Johnston. The value of long-term field experiments in agricultural, ecological, and environmental research. Advances in Agronomy, 1997, 59: 291–333 https://doi.org/10.1016/S0065-2113(08)60057-7
23	G E Varvel. Rotation and nitrogen fertilization effects on changes in soil carbon and nitrogen. Agronomy Journal, 1994, 86(2): 319–325 https://doi.org/10.2134/agronj1994.00021962008600020021x
24	M Lechenet, V Deytieux, D Antichi, J N Aubertot, P Bàrberi, M Bertrand, V Cellier, R Charles, C Colnenne-David, S Dachbrodt-Saaydeh, P Debaeke, T Doré, P Farcy, C Fernandez-Quintanilla, G Grandeau, C Hawes, L Jouy, E Justes, R Kierzek, P Kudsk, J R Lamichhane, F Lescourret, M Mazzoncini, B Melander, A Messéan, A C Moonen, A C Newton, J M Nolot, S Panozzo, P Retaureau, M Sattin, J Schwarz, C Toqué, V P Vasileiadis, N Munier-Jolain. Diversity of methodologies to experiment Integrated Pest Management in arable cropping systems: analysis and reflections based on a European network. European Journal of Agronomy, 2017, 83: 86–99 https://doi.org/10.1016/j.eja.2016.09.012
25	J R Teasdale, C B Coffman, R W Mangum. Potential long-term benefits of no-tillage and organic cropping systems for grain production and soil improvement. Agronomy Journal, 2007, 99(5): 1297–1305 https://doi.org/10.2134/agronj2006.0362
26	P Debaeke, N Munier-Jolain, M Bertrand, L Guichard, J M Nolot, V Faloya, P Saulas. Iterative design and evaluation of rule-based cropping systems: methodology and case studies—a review. Sustainable Agriculture, 2009, 29(1): 707–724 https://doi.org/10.1007/978-90-481-2666-8_43
27	R Chikowo, V Faloya, S Petit, N Munier-Jolain. Integrated Weed Management systems allow reduced reliance on herbicides and long-term weed control. Agriculture, Ecosystems & Environment, 2009, 132(3–4): 237–242 https://doi.org/10.1016/j.agee.2009.04.009
28	Y Zhang, L P Feng, J Wang, E L Wang, Y L Xu. Using APSIM to explore wheat yield response to climate change in the North China Plain: the predicted adaptation of wheat cultivar types to vernalization. Journal of Agricultural Science, 2013, 151(6): 836–848 https://doi.org/10.1017/S0021859612000883
29	N Colbach, L Biju-Duval, A Gardarin, S Granger, S H M Guyot, D Mézière, N Munier-Jolain, S Petit. The role of models for multicriteria evaluation and multiobjective design of cropping systems for managing weeds. Weed Research, 2014, 54(6): 541– 555 https://doi.org/10.1111/wre.12112
30	N Colbach. Modelling cropping system effects on crop pest dynamics: how to compromise between process analysis and decision aid. Plant Science, 2010, 179(1–2): 1–13 https://doi.org/10.1016/j.plantsci.2010.04.009
31	M Naika, K Shameer, R Sowdhamini. Comparative analyses of stress-responsive genes in Arabidopsis thaliana: insight from genomic data mining, functional enrichment, pathway analysis and phenomics. Molecular BioSystems, 2013, 9(7): 1888–1908 https://doi.org/10.1039/c3mb70072k pmid: 23645342
32	J E Shortridge, S M Falconi, B F Zaitchik, S D Guikema. Climate, agriculture, and hunger: statistical prediction of undernourishment using nonlinear regression and data-mining techniques. Journal of Applied Statistics, 2015, 42(11): 2367–2390 https://doi.org/10.1080/02664763.2015.1032216
33	A Tayyebi, B C Pijanowski, M Linderman, C Gratton. Comparing three global parametric and local non-parametric models to simulate land use change in diverse areas of the world. Environmental Modelling & Software, 2014, 59: 202–221 https://doi.org/10.1016/j.envsoft.2014.05.022
34	T Waheed, R B Bonnell, S O Prasher, E Paulet. Measuring performance in precision agriculture: CART-A decision tree approach. Agricultural Water Management, 2006, 84(1–2): 173–185 https://doi.org/10.1016/j.agwat.2005.12.003
35	N Colbach, F Colas, O Pointurier, W Queyrel, J Villerd. A methodology for multi-objective cropping system design based on simulations. Application to weed management. European Journal of Agronomy, 2017, 87: 59–73 https://doi.org/10.1016/j.eja.2017.04.005
36	R Tibshirani. Regression shrinkage and selection via the Lasso. Journal of the Royal Statistical Society. Series B: Methodological, 1996, 58(1): 267–288 https://doi.org/10.1111/j.2517-6161.1996.tb02080.x
37	E Lichtenberg, D Zilberman. The econometrics of damage control: why specification matters. American Journal of Agricultural Economics, 1986, 68(2): 261–273 https://doi.org/10.2307/1241427
38	J Fernandez-Cornejo, S Jans, M Smith. Issues in the economics of pesticide use in agriculture: a review of the empirical evidence. Applied Economic Perspectives and Policy, 1998, 20(2): 462– 488 https://doi.org/10.2307/1350002
39	J Wang, M Chu, Y Ma. Measuring rice farmer’s pesticide overuse practice and the determinants: a statistical analysis based on data collected in Jiangsu and Anhui Provinces of China. Sustainability, 2018, 10(3): 677 https://doi.org/10.3390/su10030677
40	Y Wu, X Xi, X Tang, D Luo, B Gu, S K Lam, P M Vitousek, D Chen. Policy distortions, farm size, and the overuse of agricultural chemicals in China. Proceedings of the National Academy of Sciences of the United States of America, 2018, 115(27): 7010–7015 https://doi.org/10.1073/pnas.1806645115 pmid: 29915067

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