Environmental classification of maize-testing sites in the SADC region and its implication for collaborative maize breeding strategies in the subcontinent

Setimela, P.S.; Chitalu, Z.; Jonazi, J.; Mambo, A.; Hodson, Dave; Bänziger, Marianne

doi:10.1007/s10681-005-0625-4

Cited by 48 publications

(58 citation statements)

References 7 publications

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“…CIMMYT divides the main maize-growing regions into mega-environments depending on their environmental conditions, most importantly temperature and rainfall conditions during the growing season 25 . In this study, we used CIMMYT's mega-environments data set for Africa, upscaled to a grid of 1.125 refs 26,27).…”

Section: Methodsmentioning

confidence: 99%

Current warming will reduce yields unless maize breeding and seed systems adapt immediately

et al. 2016

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The development of crop varieties that are better suited to new climatic conditions is vital for future food production 1,2 . Increases in mean temperature accelerate crop development, resulting in shorter crop durations and reduced time to accumulate biomass and yield 3,4 . The process of breeding, delivery and adoption (BDA) of new maize varieties can take up to 30 years. Here, we assess for the first time the implications of warming during the BDA process by using five bias-corrected global climate models and four representative concentration pathways with realistic scenarios of maize BDA times in Africa. The results show that the projected di erence in temperature between the start and end of the maize BDA cycle results in shorter crop durations that are outside current variability. Both adaptation and mitigation can reduce duration loss. In particular, climate projections have the potential to provide target elevated temperatures for breeding. Whilst options for reducing BDA time are highly context dependent, common threads include improved recording and sharing of data across regions for the whole BDA cycle, streamlining of regulation, and capacity building. Finally, we show that the results have implications for maize across the tropics, where similar shortening of duration is projected.By 2050 the majority of African countries will have significant experience of novel climates 1 . However, precise information as to when novel climates will occur has not been available until the recent development of techniques to identify the time of emergence of climate change signals 5,6 . These techniques quantify the signal of a change in climate relative to the background 'noise' of current climate variability. Metrics that capture the response of crops to single or multiple aspects of weather or climate (crop-climate indices 7 ) are another tool that has been developed intensively in recent years. Alongside crop yield modelling, these techniques now enable assessments of the projected times at which climate change will alter crop productivity. These alterations are mediated through both crop growth (that is, photosynthesis and biomass accumulation) and development (phenological and morphological responses).We use seven crop-climate indices (Supplementary Table S2) to identify when heat stress, drought stress and crop duration (that is, time from germination to maturity) become systematically and significantly outside the ranges at present experienced by maize cultivation in sub-Saharan Africa. Crop breeders have long been aware of the need to develop new crop varieties that are suited to future climates, particularly with respect to heat and drought stress 8,9 . Heat stress impacts are evident in our analysis. However, heat stress indices are not sufficiently constrained at present (that is, uncertainty in their values is too great) for detection of a climate change signal; only the signal in crop duration changes exceeded the noise of climate variability and thus showed a time of emergence within this century (see...

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Section: Methodsmentioning

confidence: 99%

Current warming will reduce yields unless maize breeding and seed systems adapt immediately

et al. 2016

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“…Therefore, for breeding purposes it is ideal to classify the production environments into megaenvironments to see which varieties are adapted to which mega-environment. Setimela et al (2005) grouped maize production environments in southern Africa into different megaenvironments based on data from the International Maize and Wheat Improvement Center (CIMMYT) regional maize trials, and the maize-growing areas in Zimbabwe were divided into several mega-environments. Indeed, Zimbabwe has diverse agro-ecological zones that are classified into five natural regions based on their potential for crop production (Rukuni et al 2006).…”

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confidence: 99%

“…Indeed, Zimbabwe has diverse agro-ecological zones that are classified into five natural regions based on their potential for crop production (Rukuni et al 2006). Sorghum is grown in all these agro-ecological zones, which are highly variable in terms of soil characteristics, rainfall and temperature, among other factors (Nyamapfene 1991;Setimela et al 2005;Rukuni et al 2006). The sorghum breeding programs in Zimbabwe therefore target development of new varieties with superior agronomic performance suitable for the five agro-ecological zones.…”

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confidence: 99%

Evaluation of the performance of sorghum genotypes using GGE biplot

GasuraEdmore

SetimelaPeter²,

SoutaCaleb³

2015

Can. J. Plant Sci.

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Gasura, E., Setimela, P. S. and Souta, C. M. 2015. Evaluation of the performance of sorghum genotypes using GGE biplot. Can. J. Plant Sci. 95: 1205Á1214. In spite of sorghum's drought tolerance, it is largely affected by genotype)environment interaction (GE), making it difficult and expensive to select and recommend new sorghum genotypes for different environments. The objectives of this study were to examine the nature of GE for sorghum grain yield, to identify superior sorghum genotypes for sorghum production environments and determine ideal testing locations for future breeding activities in Zimbabwe. The grain yield of 20 sorghum genotypes from Seed Co. Pvt. Ltd. were evaluated for 2 yr (2011/2012 and 2012/ 2013 cropping seasons) at five locations in different agro-ecological zones of Zimbabwe. Combined analyses of variance showed significant differences for genotypes (PB0.01), environments (PB0.001) and genotype )location (PB0.01). Genotype )environment variance component was seven times greater than that of genotypes. Genotype)environment interaction was attributed to the variability in the predictable biotic and abiotic factors associated with the different locations. The genotype main effect plus GE biplot showed that the experimental sorghum genotypes W07, W09, W05, G06 and OP46 were high yielding and stable, and possessed other desirable agronomic traits. The most discriminating and representative location was Rattray Arnold Research Station.

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“…Differences among cluster analysis by genotypic performances and geo-climatic zoning have been described in several species, including maize (Setimela et al 2005) and have been ascribed to GEI effects. In fact, complex GEI effects were observed in comparison with local selection (Table 5).…”

Section: Resultsmentioning

confidence: 99%

“…In several breeding programs, environments were classified based on cultivar performance and evaluated in a broad range of environments, focusing on the effects of genotype environment interaction (GEI) (Bernardo 2002, Löffler et al 2005. Environments were hierarchically grouped, based on dissimilarity measures (Ouyang et al 1995, Setimela et al 2005. Complex and significant GEI have been detected in regional trials across Paraná (Gerage et al 2005).…”

Section: Introductionmentioning

confidence: 99%

Environment and genotype-environment interaction in maize breeding in Paraná, Brazil

Júnior¹,

Vencovsky²,

Koehler³

2008

CBAB

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Environmental classification of maize-testing sites in the SADC region and its implication for collaborative maize breeding strategies in the subcontinent

Cited by 48 publications

References 7 publications

Current warming will reduce yields unless maize breeding and seed systems adapt immediately

Current warming will reduce yields unless maize breeding and seed systems adapt immediately

Evaluation of the performance of sorghum genotypes using GGE biplot

Environment and genotype-environment interaction in maize breeding in Paraná, Brazil

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