scholarly journals Beat the stress: breeding for climate resilience in maize for the tropical rainfed environments

2021 ◽  
Author(s):  
Boddupalli M. Prasanna ◽  
Jill E. Cairns ◽  
P. H. Zaidi ◽  
Yoseph Beyene ◽  
Dan Makumbi ◽  
...  
Keyword(s):  
Insect Pests ◽  
Critical Role ◽  
Sub Saharan Africa ◽  
Tropical Maize ◽  
Maize Breeding ◽  
Genetic Gains ◽  
Maize Cultivars ◽  
Climate Resilience ◽  
Maize Varieties ◽  
The Tropics

Abstract Key message Intensive public sector breeding efforts and public-private partnerships have led to the increase in genetic gains, and deployment of elite climate-resilient maize cultivars for the stress-prone environments in the tropics. Abstract Maize (Zea mays L.) plays a critical role in ensuring food and nutritional security, and livelihoods of millions of resource-constrained smallholders. However, maize yields in the tropical rainfed environments are now increasingly vulnerable to various climate-induced stresses, especially drought, heat, waterlogging, salinity, cold, diseases, and insect pests, which often come in combinations to severely impact maize crops. The International Maize and Wheat Improvement Center (CIMMYT), in partnership with several public and private sector institutions, has been intensively engaged over the last four decades in breeding elite tropical maize germplasm with tolerance to key abiotic and biotic stresses, using an extensive managed stress screening network and on-farm testing system. This has led to the successful development and deployment of an array of elite stress-tolerant maize cultivars across sub-Saharan Africa, Asia, and Latin America. Further increasing genetic gains in the tropical maize breeding programs demands judicious integration of doubled haploidy, high-throughput and precise phenotyping, genomics-assisted breeding, breeding data management, and more effective decision support tools. Multi-institutional efforts, especially public–private alliances, are key to ensure that the improved maize varieties effectively reach the climate-vulnerable farming communities in the tropics, including accelerated replacement of old/obsolete varieties.

scholarly journals Genomic Prediction of Tropical Maize Resistance to Fall Armyworm and Weevils: Genomic Selection Should Focus on Effective Training Set Determination

2020 ◽  
Author(s):  
Arfang Badji ◽  
Lewis Machida ◽  
Daniel Bomet Kwemoi ◽  
Frank Kumi ◽  
Dennis Okii ◽  
...  
Keyword(s):  
Genomic Selection ◽  
Genomic Prediction ◽  
Insect Pests ◽  
Fall Armyworm ◽  
Sub Saharan Africa ◽  
Tropical Maize ◽  
Training Set ◽  
Maize Weevil ◽  
Maize Resistance ◽  
Sub Saharan

Genomic selection (GS) can accelerate variety release by shortening variety development phase when factors that influence prediction accuracies (PA) of genomic prediction (GP) models such as training set (TS) size and relationship with the breeding set (BS) are optimized beforehand. In this study, PAs for the resistance to fall armyworm (FAW) and maize weevil (MW) in a diverse tropical maize panel composed of 341 double haploid and inbred lines were estimated. Both phenotypic best linear unbiased predictors (BLUPs) and estimators (BLUEs) were predicted using 17 parametric, semi-parametric, and nonparametric algorithms with a 10-fold and 5 repetitions cross-validation strategy. n. For both MW and FAW resistance datasets with an RBTS of 37%, PAs achieved with BLUPs were at least as twice as higher than those realized with BLUEs. The PAs achieved with BLUPs for MW resistance traits: grain weight loss (GWL), adult progeny emergence (AP), and number of affected kernels (AK) varied from 0.66 to 0.82. The PAs were also high for FAW resistance RBTS datasets, varying from 0.694 to 0.714 (for RBTS of 37%) to 0.843 to 0.844 (for RBTS of 85%). The PAs for FAW resistance with PBTS were generally high varying from 0.83 to 0.86, except for one dataset that had PAs ranging from 0.11 to 0.75. GP models showed generally similar predictive abilities for each trait while the TS designation was determinant. There was a highly positive correlation (R=0.92***) between TS size and PAs for the RBTS approach while, for the PBTS, these parameters were highly negatively correlated (R=-0.44***), indicating the importance of the degree of kinship between the TS and the BS with the smallest TS (31%) achieving the highest PAs (0.86). This study paves the way towards the use of GS for maize resistance to insect pests in sub-Saharan Africa.

scholarly journals Implications of farmer perceived production constraints and varietal preferences to pearl millet breeding in Senegal.

1970 ◽  
Vol 28 (3) ◽  
pp. 411-420
Author(s):  
G. Kanfany ◽  
O. Diack ◽  
N.A. Kane ◽  
P. I. Gangashetty ◽  
O. Sy ◽  
...  
Keyword(s):  
Rural Communities ◽  
Soil Fertility ◽  
Pearl Millet ◽  
Insect Pests ◽  
Pennisetum Glaucum ◽  
Critical Role ◽  
Plant Diseases ◽  
Sub Saharan Africa ◽  
Production Constraints ◽  
Varietal Preference

Pearl millet (Pennisetum glaucum L.) plays a critical role in smallholder food security in sub-Saharan Africa. The production of pearl millet has, however, stagnated or even declined due to several factors. The objective of this study was to assess farmer perceptions on production constraints and varietal preferences in Senegal. A survey was conducted involving 150 randomly selected farmers from 15 villages, in five representative rural communities of Senegal. A semi-structured questionnaire was used, supplemented by focus group discussions. Results revealed that parasitic Striga weed was the most constraining factor to pearl millet production across the rural communes. This was followed by low soil fertility and insect pests in that order. Other constraints included lack of machinery for sowing, plant diseases, drought, seed-eating birds, limited access to land for pearl millet cultivation and limited seed availability. Among the traits for varietal preference, farmers unanimously considered grain yield as the most important trait. Other important traits mentioned were adaptation to drought, adaptation to low soil fertility and earliness. These production constraints and varietal preference should be integrated in the profile of the national pearl millet breeding programmes in order to improve the productivity and adoption of bred-cultivars.

scholarly journals Factors Influencing Genomic Prediction Accuracies of Tropical Maize Resistance to Fall Armyworm and Weevils

Plants ◽  
2020 ◽  
Vol 10 (1) ◽  
pp. 29
Author(s):  
Arfang Badji ◽  
Lewis Machida ◽  
Daniel Bomet Kwemoi ◽  
Frank Kumi ◽  
Dennis Okii ◽  
...  
Keyword(s):  
Genomic Prediction ◽  
Insect Pests ◽  
Fall Armyworm ◽  
Sub Saharan Africa ◽  
Tropical Maize ◽  
Maize Weevil ◽  
Individual Traits ◽  
Maize Resistance ◽  
Sub Saharan ◽  
Training Sets

Genomic selection (GS) can accelerate variety improvement when training set (TS) size and its relationship with the breeding set (BS) are optimized for prediction accuracies (PAs) of genomic prediction (GP) models. Sixteen GP algorithms were run on phenotypic best linear unbiased predictors (BLUPs) and estimators (BLUEs) of resistance to both fall armyworm (FAW) and maize weevil (MW) in a tropical maize panel. For MW resistance, 37% of the panel was the TS, and the BS was the remainder, whilst for FAW, random-based training sets (RBTS) and pedigree-based training sets (PBTSs) were designed. PAs achieved with BLUPs varied from 0.66 to 0.82 for MW-resistance traits, and for FAW resistance, 0.694 to 0.714 for RBTS of 37%, and 0.843 to 0.844 for RBTS of 85%, and these were at least two-fold those from BLUEs. For PBTS, FAW resistance PAs were generally higher than those for RBTS, except for one dataset. GP models generally showed similar PAs across individual traits whilst the TS designation was determinant, since a positive correlation (R = 0.92***) between TS size and PAs was observed for RBTS, and for the PBTS, it was negative (R = 0.44**). This study pioneered the use of GS for maize resistance to insect pests in sub-Saharan Africa.

scholarly journals Factors Influencing Genomic Prediction Accuracies of Tropical Maize Resistance to Fall Armyworm and Weevils

2020 ◽  
Author(s):  
Arfang Badji ◽  
Lewis Machida ◽  
Daniel Bomet Kwemoi ◽  
Frank Kumi ◽  
Dennis Okii ◽  
...  
Keyword(s):  
Genomic Prediction ◽  
Insect Pests ◽  
Fall Armyworm ◽  
Sub Saharan Africa ◽  
Tropical Maize ◽  
Maize Weevil ◽  
Phenotypic Data ◽  
Best Linear Unbiased ◽  
Maize Resistance ◽  
Best Linear Unbiased Estimators

Genomic selection (GS) can accelerate variety release by shortening the variety development phase when factors that influence prediction accuracies (PA) of genomic prediction (GP) models such as training set (TS) size and relationship with the breeding set (BS) are optimized beforehand. In this study, PAs for the resistance to fall armyworm (FAW) and maize weevil (MW) in a diverse tropical maize panel composed of 341 double haploid and inbred lines were estimated using 16 parametric, semi-parametric, and nonparametric algorithms with a 10-fold and 5 repetitions cross-validation strategy. For MW resistance, 126 lines that had both genotypic and phenotypic data were used as a TS (37% of the panel) and the remaining lines, with only genotypic data, as a BS. Regarding FAW damage resistance, two TS determination strategies, namely: random-based TS (RBTS) with increasing sizes (37, 63, 75, and 85%) and pedigree-based TS (PBTS) were used. For both MW and FAW resistance datasets with an RBTS of 37%, PAs achieved with phenotypic best linear unbiased predictors were at least as twice as higher than those realized with best linear unbiased estimators. The PAs achieved with BLUPs for MW resistance traits varied from 0.66 to 0.82. The PAs with BLUPs for FAW resistance datasets ranged from 0.694 to 0.714 for RBTS of 37%, and 0.843 to 0.844 for RBTS of 85%. The PAs with BLUPs for FAW resistance with PBTS were generally high varying from 0.83 to 0.86, except for the third dataset which had the largest TS (86.22% of the panel) with PAs ranging from 0.11 to 0.75. GP models showed generally similar predictive abilities for each trait while the TS designation was determinant. There was a highly positive correlation (R=0.92***) between TS size and PAs for the RBTS approach while, for the PBTS, these parameters were highly negatively correlated (R=-0.44***), indicating the importance of the relationship between the TS and the BS with the smallest TS (31%) achieving the highest PAs (0.86). This study paves the way towards the use of GS for maize resistance to insect pests in sub-Saharan Africa.

scholarly journals Revisiting strategies to incorporate gender-responsiveness into maize breeding in southern Africa

2021 ◽  
pp. 003072702110454
Author(s):  
Jill E Cairns ◽  
Frédéric Baudron ◽  
Kirsty L. Hassall ◽  
Thokozile Ndhlela ◽  
Isaiah Nyagumbo ◽  
...  
Keyword(s):  
Southern Africa ◽  
Management Practices ◽  
Household Head ◽  
Sub Saharan Africa ◽  
Maize Breeding ◽  
Agronomic Practices ◽  
Agronomic Management ◽  
Maize Varieties ◽  
On Farm ◽  
Trait Preferences

In sub-Saharan Africa there is increasing focus on identifying women’s trait preferences within crop breeding to enable gender-responsive product development. In the case of maize, breeding programs are ready to incorporate specific traits to increase gender-responsiveness but lack guidance on what these specific traits might be. We propose an inductive approach to determine a pathway towards increasing gender-responsiveness within maize breeding. A survey of 306 farmers was conducted to determine gender differences in maize varieties used together with key agronomic practices. Variety was a significant predictor of the gender of the plot manager and of the household head in contrast to previous surveys conducted in researcher-led on-farm trials. On-farm trials are conducted using pre-defined agronomic management practices and preferences identified at harvest are likely to centre around yield. This study highlighted significant differences in several agronomic practices used by female plot managers and female household heads. Although further studies are required to understand preferences associated with varietal choice, our results suggest that current researcher-led on-farm trials may not identify gender-specific trait preferences driving varietal choice. Furthermore, a trait-specific approach is not the only avenue towards increasing gender-responsiveness in maize breeding in southern Africa. The scope for increasing gender-intentionality in maize breeding could be expanded to incorporate selection environments more relevant to agronomic management practices used by female plot managers and households at advanced stages of the breeding pipeline. This approach could provide an immediate entry point to increase gender-intentional maize breeding in southern Africa.

scholarly journals Factors Influencing Genomic Prediction Accuracies of Tropical Maize Resistance to Fall Armyworm and Weevils

2020 ◽  
Author(s):  
Arfang Badji ◽  
Lewis Machida ◽  
Daniel Bomet Kwemoi ◽  
Frank Kumi ◽  
Dennis Okii ◽  
...  
Keyword(s):  
Genomic Prediction ◽  
Insect Pests ◽  
Fall Armyworm ◽  
Sub Saharan Africa ◽  
Tropical Maize ◽  
Maize Weevil ◽  
Individual Traits ◽  
Maize Resistance ◽  
Sub Saharan ◽  
Training Sets

Genomic selection (GS) can accelerate variety improvement when training set (TS) size, and its relationship with the breeding set (BS) are optimized for prediction accuracies (PA) of genomic prediction (GP) models. Sixteen GP algorithms were run on phenotypic best linear unbiased predictors (BLUPs) and estimators (BLUEs) of resistance to both fall armyworm (FAW) and maize weevil (MW) in a tropical maize panel. For MW resistance, 37% of the panel was the TS, and BS was the remainder whilst for FAW, random-based training sets (RBTS) and pedigree-based training sets (PBTS) were designed. PAs achieved with BLUPs varied from 0.66 to 0.82 for MW resistance traits, and, for FAW resistance, 0.694 to 0.714 for RBTS of 37%, and 0.843 to 0.844 for RBTS of 85%, and, these were at least two-fold those from BLUEs. For PBTS, FAW resistance PAs were generally higher than those for RBTS, except for one dataset. GP models generally showed similar PAs across individual traits whilst the TS designation was determinant since a positive correlation (R=0.92***) between TS size and PAs was observed for RBTS and, for the PBTS, it was negative (R=0.44**). This study pioneers the use of GS for maize resistance to insect pests in sub-Saharan Africa.

scholarly journals Genetic Gains to Grain Yield of Maize Varieties for Small Farmers in Brazil

2020 ◽  
pp. 1-12
Author(s):  
Ana Paula Cândido Gabriel Berilli ◽  
Rafael Nunes de Almeida ◽  
Luis Eduardo Gottardo ◽  
Monique Moreira Moulin ◽  
Messias Gonzaga Pereira ◽  
...  
Keyword(s):  
Genetic Variability ◽  
Recurrent Selection ◽  
Food Sovereignty ◽  
Block Design ◽  
Reciprocal Recurrent Selection ◽  
Maize Breeding ◽  
Genetic Gains ◽  
Maize Populations ◽  
Maize Varieties ◽  
Small Producers

The demand for expanding the genetic base in working collections of older maize breeding programs points to the need to pool efforts and reaffirm methodologies for conserving genetic variability that can still be accessed in maize populations. The objective of the work was to select full sib maize progenies and to estimate genetic gains in the first cycle of reciprocal recurrent selection for common maize intended for cultivation in a region characterized by family farm, in Brazil. We evaluated 120 full sib families of maize from crossbreeding between individuals of the Cimmyt and Piranão varieties. Competition trials were conducted at two experimental stations in the state of Espírito Santo, Brazil. A randomized block design was adopted with 2 repetitions, arranged in sets. Different selection indexes were tested in order to enhance gains in productivity and prolificacy. The selection of 40 superior families made it possible to estimate a gain of 0.77 Mg ha-1 in grain productivity for producers in the region. With these results, we discussed the importance of work to improve maize populations for small producers to motivate the conservation of the genetic variability of tropical maize germplasm. Thus, from the results obtained in this study, we show the possibility of investing in technologies aimed at small producers in order to motive the conservation of genetic resources, food sovereignty of producers and, consequently, world security, given the importance of this culture for human feed.

scholarly journals Maize Breeding in the Highlands of Ecuador, Peru, and Bolivia: A Review

Agronomy ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 212
Author(s):  
José Luis Zambrano ◽  
Carlos F. Yánez ◽  
Carlos A. Sangoquiza
Keyword(s):  
Agricultural Research ◽  
Public Institutions ◽  
Human Consumption ◽  
Participatory Plant Breeding ◽  
Maize Breeding ◽  
Andean Region ◽  
Breeding Programs ◽  
Maize Cultivars ◽  
Maize Varieties ◽  
Breeding Techniques

Maize is one of the most important staple crops in the highlands of the Andean region of Ecuador, Peru, and Bolivia. Most seeds come from landraces, with their own kernel characteristics. The kernels are used for the elaboration of traditional dishes and other elaborates for human consumption. In this region, maize breeding is conducted mainly by public institutions. In this review, we outline the methodology that has been used by the maize breeding programs (MBPs) of the National Institutes for Agricultural Research and other institutions in the highlands of Ecuador, Peru, and Bolivia during the last 20 years. The main objective of MBPs in the region has been to develop more uniform and productive open-pollinated varieties (OPVs) of floury maize (Zea mays L. var. Amylacea), which is the most important type of maize in the area. Participatory plant breeding, combined with half-sib, has been used to breed new maize varieties. At least 18 OPVs of floury maize have been released into the Andean region in the last 20 years. Breeding this type of maize has been very important to conserve diversity and promote consumption in the region, but they have had very little impact on yield. The yield of floury maize is around three times below that of dent or semident maize grown in the region. Therefore, there is a need to apply new breeding techniques in the region to accelerate the development of more productive floury-maize cultivars.

scholarly journals HANDY: a device for assessing resistance to mechanical crushing of maize kernel

Plant Methods ◽  
2021 ◽  
Vol 17 (1) ◽  
Author(s):  
Yuan Su ◽  
Yang Xu ◽  
Tao Cui ◽  
Xiaojun Gao ◽  
Guoyi Xia ◽  
...  
Keyword(s):  
Moisture Content ◽  
Goodness Of Fit ◽  
Function Analysis ◽  
Physical Damage ◽  
Maize Kernel ◽  
Rotating Speed ◽  
Maize Cultivars ◽  
Maize Varieties ◽  
New Perspective ◽  
Mechanical Crushing

Abstract Background How to control the physical damage during maize kernel harvesting is a major problem for both mechanical designers and plant breeders. A limitation of addressing this problem is lacking a reliable method for assessing the relation between kernel damage susceptibility and threshing quality. The design, construction, and testing of a portable tool called “HANDY”, which can assess the resistance to mechanical crushing in maize kernel. HANDY can impact the kernel with a special accelerator at a given rotating speed and then cause measurable damage to the kernel. These factors are varied to determine the ideal parameters for operating the HANDY. Results Breakage index (BI, target index of HANDY), decreased as the moisture content of kernel increased or the rotating speed decreased within the tested range. Furthermore, the HANDY exhibited a greater sensitivity in testing kernels at higher moisture level influence on the susceptibility of damage kernel than that in Breakage Susceptibility tests, particularly when the centrifugation speed is about 1800 r/min and the centrifugal disc type is curved. Considering that the mechanical properties of kernels vary greatly as the moisture content changes, a subsection linear (average goodness of fit is 0.9) to predict the threshing quality is built by piecewise function analysis, which is divided by kernel moisture. Specifically, threshing quality is regarded as a function of the measured result of the HANDY. Five maize cultivars are identified with higher damage resistance among 21 tested candidate varieties. Conclusions The HANDY provides a quantitative assessment of the mechanical crushing resistance of maize kernel. The BI is demonstrated to be a more robust index than breakage susceptibility (BS) when evaluating threshing quality in harvesting in terms of both reliability and accuracy. This study also offers a new perspective for evaluating the mechanical crushing resistance of grains and provides technical support for breeding and screening maize varieties that are suitable for mechanical harvesting.

scholarly journals Observed and estimated leaf appearance of landrace and improved maize cultivars

2017 ◽  
Vol 48 (1) ◽  
Author(s):  
Josana Andreia Langner ◽  
Nereu Augusto Streck ◽  
Angelica Durigon ◽  
Stefanía Dalmolin da Silva ◽  
Isabel Lago ◽  
...  
Keyword(s):  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Sowing Date ◽  
Mean Square ◽  
Data Set ◽  
Growing Seasons ◽  
Maize Cultivars ◽  
Maize Varieties ◽  
Leaf Appearance

ABSTRACT: The objective of this study was to compare the simulations of leaf appearance of landrace and improved maize cultivars using the CSM-CERES-Maize (linear) and the Wang and Engel models (nonlinear). The coefficients of the models were calibrated using a data set of total leaf number collected in the 11/04/2013 sowing date for the landrace varieties ‘Cinquentinha’ and ‘Bico de Ouro’ and the simple hybrid ‘AS 1573PRO’. For the ‘BRS Planalto’ variety, model coefficients were estimated with data from 12/13/2014 sowing date. Evaluation of the models was with independent data sets collected during the growing seasons of 2013/2014 (Experiment 1) and 2014/2015 (Experiment 2) in Santa Maria, RS, Brazil. Total number of leaves for both landrace and improved maize varieties was better estimated with the Wang and Engel model, with a root mean square error of 1.0 leaf, while estimations with the CSM-CERES-Maize model had a root mean square error of 1.5 leaf.

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