Untangling crop management and environmental influences on wheat yield variability in Bangladesh: An application of non-parametric approaches
Version: 1– Released: Mon Sep 25 06:10:36 CDT 2017
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Data Citation
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Timothy J. Krupnik, Zia Uddin Ahmed, Jagadish Timsina, Samina Yasmin, Farhad Hossain, Abdullah Al Mamun, Aminul Islam Mridha, Andrew J. McDonald, Untangling crop management and environmental influences on wheat yield variability in Bangladesh: An application of non-parametric approaches, Agricultural Systems, Volume 139, 2015, Pages 166-179, ISSN 0308-521X, http://dx.doi.org/10.1016/j.agsy.2015.05.007.
ID: DOI:10.1016/j.agsy.2015.05.007
Data Citation Details
Study Global IDhdl:11529/11037
AuthorsTimothy J. Krupnik (International Maize and Wheat Improvement Center); Zia Uddin Ahmed (International Maize and Wheat Improvement Center); Jagadish Timsina (Faculty of Veterinary and Agricultural Sciences, University of Melbourne, Victoria 3010, Australia); Samina Yasmin (International Maize and Wheat Improvement Center); Farhad Hossain (International Maize and Wheat Improvement Center); Abdullah Al Mamun (International Maize and Wheat Improvement Center); Aminul Islam Mridha (International Maize and Wheat Improvement Center); Andrew J. McDonald (International Maize and Wheat Improvement Center)
ProducerInternational Maize and Wheat Improvement Center (CIMMYT) Logo; International Rice Research Institute (IRRI) Logo; International Food Policy Research Institute (IFPRI) Logo
Production DateNovember, 2014
Production PlaceBangladesh
Software R
Funding AgencyUSAID and Bill & Melinda Gates Foundation (BMGF)
DistributorInternational Maize and Wheat Improvement Center (CIMMYT) Logo; International Rice Research Institute (IRRI) Logo; International Food Policy Research Institute (IFPRI) Logo
Distributor ContactTimothy J. Krupnik (CIMMYT Bangladesh), t.krupnik@cgiar.org
Distribution DateJune, 2015
DepositorAshok Rai
Deposit DateAugust 19, 2017
Description and Scope

In South Asia, wheat is typically grown in favorable environments, although policies promoting intensification in Bangladesh's stress-prone coastal zone have resulted in expanded cultivation in this non-traditional area.

Relatively little is known about howto best manage wheat in these unique environments. Research is thus needed to identify ‘best-bet’ entry points to optimize productivity, but classical parametric analyses offer limited applicability to elucidate the relative importance of the multiple factors and interactions that influence yield under such conditions. This problem is most evident in datasets derived from farmer-participatory research, where missing values and skewed data are common.

This paper examines the predictive power of three nonparametric approaches, including linear mixed effects models (LMMs), and two binary recursive partitioning methods: classification and regression trees (CARTs)and Random Forests

We collected yield, crop management, and environmental observations from 422 wheat fields in the 2012–13 season, across six production environments spanning southern Bangladesh, where nutrient rates and genotypes were imposed, but management of other production factors varied from farmer to farmer. Fields were grouped into categories including early and late-sowing, depending on crop establishment before or after December 15, respectively, and in combination, across both early- and late-sowing groups.

For each of these groups, we investigated how each non-parametric analysis predicted the factors influencing yield. All three approaches identified nitrogen rate and environment as the most important factors, regardless of sowing category. CART also identified assemblages of high- and low-yielding environments, although those located in saline and warmer thermal zones were not necessarily the lowest yielding, indicating that farmers can optimize crop management to overcome these constraints.

The number of days farmers sowed wheat before or after December 15, days to maturity, and the number of irrigations and weedings also influenced yield, though each method weighted these factors differently.

LMMs also indicated a slight yield advantage when farmers used stress-tolerant genotypes, though CART and Random Forests did not. One-to-one plots for observed vs. predicted yields from LMMs and Random Forests showed better performance by the former than the latter, with smaller root mean square and mean absolute error for the combined, early- and late-sowing groups, respectively.

While the LMMs were superior in this case, Random Forests may still prove useful in the classification and interpretation of farm survey data in which no treatment interventions have been administered.

Description DateMay, 2015
Time Period Covered2012 - 2013
Date of Collection2012 - 2013
Geographic UnitFarmer's field
Kind of DataYield, crop management and environmental observation data
Data Collection / Methodology
Sampling Procedure

This research involved 48 participating wheat farmers (eight per environment). Within each field studied, farmers imposed different nitrogen (N) rates and trialed two new genotypes in comparison to a traditionally grown local check. In contrast to northern Bangladesh, where optimal wheat sowing time is November 15, Krupnik et al. (2015) recommended sowing around mid-December in southern Bangladesh because of the late land availability following the harvesting of long-duration rice varieties and prolonged monsoon-season flooding.

We consequently chose 15 December as the mid-point for sowing, and grouped four farmers as early- (before December 15) and late- (after December 15) sowers in each environment. Each farmer managed several plots within their fields, resulting in 432 observational units. From this, we excluded 10 outliers, resulting in 422 final observations. Farmers sowed wheat as soon as possible when soil moisture was optimal for tillage, with sowing dates varying across farmers and environments. The range for sowing in Sadar, Fultala and Kalapara was large, from November 20 to January 5, while in Ujirpur, it was smaller, from December 5 to 25.

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Number of Files 27
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"Untangling crop management and environmental influences on wheat yield variability in Bangladesh: An application of non-parametric approaches", hdl:11529/11037