Predictors determining the potential of inland valleys for rice production development in West Africa

被引:15
作者
Djagba, Justin Fagnombo [1 ,2 ]
Sintondji, Luc O. [3 ]
Kouyate, Amadou Male [4 ]
Baggie, Idriss [5 ]
Agbahungba, Georges [2 ]
Hamadoun, Abdoulaye [4 ]
Zwart, Sander J. [6 ]
机构
[1] Africa Rice Ctr AfricaRice, 01 BP 2031, Cotonou, Benin
[2] Univ Abomey Calavi, ICMPA, UNESCO Chair, 072 BP 50, Cotonou, Benin
[3] Univ Abomey Calavi, Natl Inst Water, Hydraul & Water Control Lab, 01 BP 526, Cotonou, Benin
[4] IER, Reg Agr Res Ctr Sikasso, BP 16, Sikasso, Mali
[5] SLARI, Tower Hill,PMB 1313, Freetown, Sierra Leone
[6] Univ Twente, Fac Geoinformat Sci & Earth Observat ITC, POB 217, NL-7500 AE Enschede, Netherlands
关键词
Inland valley; Rice production; Predictor; Relevant variable selection; Random forests; RANDOM FORESTS; AGROECOLOGICAL GRADIENT; AGRICULTURAL SYSTEMS; CLASSIFICATION; CULTIVATION; SELECTION; DYNAMICS; IMPACT; CROPS;
D O I
10.1016/j.apgeog.2018.05.003
中图分类号
P9 [自然地理学]; K9 [地理];
学科分类号
0705 ; 070501 ;
摘要
Water availability and high soil fertility make inland valley landscapes suitable for sustainable rice-based cropping. In this study, Random Forests statistical analysis was used on a database of 499 surveyed inland valleys in four study zones in three West African countries. The goal of the study was to assess parameters that indicate (are predictors for) high potential for development of rice-based systems in inland valleys. These parameters are related to the biophysical (hydrology, soil, climate, and topography) and socio-economic (demography, accessibility, and markets) environments. Farmer group surveys and secondary data from existing publicly available spatial data sets were used. The analysis revealed that, across the four research areas, the following parameters were relevant predictors for rice development: (1) distance from the inland valley to the nearest market; (2) distance from the inland valley to the nearest rice mill; (3) population density in the immediate environment of the inland valley; (4) total nitrogen in the top 20 cm of the soil profile; (5) land elevation; and (6) soil texture on the upper slope of the inland valley. Several predictors were highly important for specific research areas, but not for all, thus showing the diversity in the studied agricultural landscapes. These predictors included soil fertility management, source of irrigation water, and the percentage of female farmers in the inland valley. The identified relevant predictors will be used to map the potential rice production development of the inland valleys. This will help development agencies to assess their zones based on quantitative analysis for inland valley potential development.
引用
收藏
页码:86 / 97
页数:12
相关论文
共 75 条
  • [1] Soil Fertility Potential for Rice Production in West African Lowlands
    Abe, Susumu S.
    Buri, M. Moro
    Issaka, Roland N.
    Kiepe, Paul
    Wakatsuki, Toshiyuki
    [J]. JARQ-JAPAN AGRICULTURAL RESEARCH QUARTERLY, 2010, 44 (04): : 343 - 355
  • [2] AfricaRice, 2014, AFR RIC TRENDS 2001, P108
  • [3] Application of the Random Forest Method to Analyse Epidemiological and Phenotypic Characteristics of Salmonella 4,[5],12:i:- and Salmonella Typhimurium Strains
    Barco, L.
    Mancin, M.
    Ruffa, M.
    Saccardin, C.
    Minorello, C.
    Zavagnin, P.
    Lettini, A. A.
    Olsen, J. E.
    Ricci, A.
    [J]. ZOONOSES AND PUBLIC HEALTH, 2012, 59 (07) : 505 - 512
  • [4] Overview of random forest methodology and practical guidance with emphasis on computational biology and bioinformatics
    Boulesteix, Anne-Laure
    Janitza, Silke
    Kruppa, Jochen
    Koenig, Inke R.
    [J]. WILEY INTERDISCIPLINARY REVIEWS-DATA MINING AND KNOWLEDGE DISCOVERY, 2012, 2 (06) : 493 - 507
  • [5] Random forest Gini importance favours SNPs with large minor allele frequency: impact, sources and recommendations
    Boulesteix, Anne-Laure
    Bender, Andreas
    Bermejo, Justo Lorenzo
    Strobl, Carolin
    [J]. BRIEFINGS IN BIOINFORMATICS, 2012, 13 (03) : 292 - 304
  • [6] Random forests
    Breiman, L
    [J]. MACHINE LEARNING, 2001, 45 (01) : 5 - 32
  • [7] Markets, Climate Change, and Food Security in West Africa
    Brown, Molly E.
    Hintermann, Beat
    Higgins, Nathaniel
    [J]. ENVIRONMENTAL SCIENCE & TECHNOLOGY, 2009, 43 (21) : 8016 - 8020
  • [8] Calle M Luz, 2011, Brief Bioinform, V12, P86, DOI 10.1093/bib/bbq011
  • [9] Application of Random Forests Methods to Diabetic Retinopathy Classification Analyses
    Casanova, Ramon
    Saldana, Santiago
    Chew, Emily Y.
    Danis, Ronald P.
    Greven, Craig M.
    Ambrosius, Walter T.
    [J]. PLOS ONE, 2014, 9 (06):
  • [10] Analysis and Prediction of Highly Effective Antiviral Peptides Based on Random Forests
    Chang, Kuan Y.
    Yang, Je-Ruei
    [J]. PLOS ONE, 2013, 8 (08):