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Khanna A, Anumalla M, Ramos J, Cruz MTS, Catolos M, Sajise AG, Gregorio G, Dixit S, Ali J, Islam MR, Singh VK, Rahman MA, Khatun H, Pisano DJ, Bhosale S, Hussain W. Genetic gains in IRRI's rice salinity breeding and elite panel development as a future breeding resource. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2024; 137:37. [PMID: 38294550 PMCID: PMC10830834 DOI: 10.1007/s00122-024-04545-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Accepted: 01/05/2024] [Indexed: 02/01/2024]
Abstract
KEY MESSAGE Estimating genetic gains and formulating a future salinity elite breeding panel for rice pave the way for developing better high-yielding salinity tolerant lines with enhanced genetic gains. Genetic gain is a crucial parameter to check the breeding program's success and help optimize future breeding strategies for enhanced genetic gains. To estimate the genetic gains in IRRI's salinity breeding program and identify the best genotypes based on high breeding values for grain yield (kg/ha), we analyzed the historical data from the trials conducted in the IRRI, Philippines and Bangladesh. A two-stage mixed-model approach accounting for experimental design factors and a relationship matrix was fitted to obtain the breeding values for grain yield and estimate genetic trends. A positive genetic trend of 0.1% per annum with a yield advantage of 1.52 kg/ha was observed in IRRI, Philippines. In Bangladesh, we observed a genetic gain of 0.31% per annum with a yield advantage of 14.02 kg/ha. In the released varieties, we observed a genetic gain of 0.12% per annum with a 2.2 kg/ha/year yield advantage in the IRRI, Philippines. For the Bangladesh dataset, a genetic gain of 0.14% per annum with a yield advantage of 5.9 kg/ha/year was observed in the released varieties. Based on breeding values for grain yield, a core set of the top 145 genotypes with higher breeding values of > 2400 kg/ha in the IRRI, Philippines, and > 3500 kg/ha in Bangladesh with a reliability of > 0.4 were selected to develop the elite breeding panel. Conclusively, a recurrent selection breeding strategy integrated with novel technologies like genomic selection and speed breeding is highly required to achieve higher genetic gains in IRRI's salinity breeding programs.
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Affiliation(s)
- Apurva Khanna
- Rice Breeding Innovation Platform, International Rice Research Institute (IRRI), 4031, Los Baños, Laguna, Philippines
| | - Mahender Anumalla
- Rice Breeding Innovation Platform, International Rice Research Institute (IRRI), 4031, Los Baños, Laguna, Philippines
| | - Joie Ramos
- Rice Breeding Innovation Platform, International Rice Research Institute (IRRI), 4031, Los Baños, Laguna, Philippines
| | - Ma Teresa Sta Cruz
- Rice Breeding Innovation Platform, International Rice Research Institute (IRRI), 4031, Los Baños, Laguna, Philippines
| | - Margaret Catolos
- Rice Breeding Innovation Platform, International Rice Research Institute (IRRI), 4031, Los Baños, Laguna, Philippines
| | - Andres Godwin Sajise
- Rice Breeding Innovation Platform, International Rice Research Institute (IRRI), 4031, Los Baños, Laguna, Philippines
| | - Glenn Gregorio
- Southeast Asian Regional Center for Graduate Study and Research in Agriculture (SEARCA) and University of Philippines, 4031, Los Baños, Laguna, Philippines
| | - Shalabh Dixit
- Rice Breeding Innovation Platform, International Rice Research Institute (IRRI), 4031, Los Baños, Laguna, Philippines
| | - Jauhar Ali
- Rice Breeding Innovation Platform, International Rice Research Institute (IRRI), 4031, Los Baños, Laguna, Philippines
| | - Md Rafiqul Islam
- IRRI South Asia Regional Center (IRRI-SA Hub), Hyderabad, Telangana, 502324, India
| | - Vikas Kumar Singh
- IRRI South Asia Regional Center (IRRI-SA Hub), Hyderabad, Telangana, 502324, India
| | - Md Akhlasur Rahman
- Plant Breeding Division, Bangladesh Rice Research Institute (BRRI), Gazipur, 1701, Bangladesh
| | - Hasina Khatun
- Plant Breeding Division, Bangladesh Rice Research Institute (BRRI), Gazipur, 1701, Bangladesh
| | - Daniel Joseph Pisano
- Rice Breeding Innovation Platform, International Rice Research Institute (IRRI), 4031, Los Baños, Laguna, Philippines
| | - Sankalp Bhosale
- Rice Breeding Innovation Platform, International Rice Research Institute (IRRI), 4031, Los Baños, Laguna, Philippines
| | - Waseem Hussain
- Rice Breeding Innovation Platform, International Rice Research Institute (IRRI), 4031, Los Baños, Laguna, Philippines.
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Tarekegne A, Wegary D, Cairns JE, Zaman-Allah M, Beyene Y, Negera D, Teklewold A, Tesfaye K, Jumbo MB, Das B, Nhamucho EJ, Simpasa K, Kaonga KKE, Mashingaidze K, Thokozile N, Mhike X, Prasanna BM. Genetic gains in early maturing maize hybrids developed by the International Maize and Wheat Improvement Center in Southern Africa during 2000-2018. FRONTIERS IN PLANT SCIENCE 2024; 14:1321308. [PMID: 38293626 PMCID: PMC10825029 DOI: 10.3389/fpls.2023.1321308] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/13/2023] [Accepted: 12/07/2023] [Indexed: 02/01/2024]
Abstract
Genetic gain estimation in a breeding program provides an opportunity to monitor breeding efficiency and genetic progress over a specific period. The present study was conducted to (i) assess the genetic gains in grain yield of the early maturing maize hybrids developed by the International Maize and Wheat Improvement Center (CIMMYT) Southern African breeding program during the period 2000-2018 and (ii) identify key agronomic traits contributing to the yield gains under various management conditions. Seventy-two early maturing hybrids developed by CIMMYT and three commercial checks were assessed under stress and non-stress conditions across 68 environments in seven eastern and southern African countries through the regional on-station trials. Genetic gain was estimated as the slope of the regression of grain yield and other traits against the year of first testing of the hybrid in the regional trial. The results showed highly significant (p< 0.01) annual grain yield gains of 118, 63, 46, and 61 kg ha-1 year-1 under optimum, low N, managed drought, and random stress conditions, respectively. The gains in grain yield realized in this study under both stress and non-stress conditions were associated with improvements in certain agronomic traits and resistance to major maize diseases. The findings of this study clearly demonstrate the significant progress made in developing productive and multiple stress-tolerant maize hybrids together with other desirable agronomic attributes in CIMMYT's hybrid breeding program.
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Affiliation(s)
- Amsal Tarekegne
- Global Maize Program, International Maize and Wheat Improvement Centre (CIMMYT), Harare, Zimbabwe
| | - Dagne Wegary
- Global Maize Program, International Maize and Wheat Improvement Centre (CIMMYT), Harare, Zimbabwe
| | - Jill E. Cairns
- Global Maize Program, International Maize and Wheat Improvement Centre (CIMMYT), Harare, Zimbabwe
| | - Mainassara Zaman-Allah
- Global Maize Program, International Maize and Wheat Improvement Centre (CIMMYT), Harare, Zimbabwe
| | - Yoseph Beyene
- Global Maize Program, International Maize and Wheat Improvement Centre (CIMMYT), Nairobi, Kenya
| | - Demewoz Negera
- Global Maize Program, International Maize and Wheat Improvement Centre (CIMMYT), Addis Ababa, Ethiopia
| | - Adefris Teklewold
- Global Maize Program, International Maize and Wheat Improvement Centre (CIMMYT), Addis Ababa, Ethiopia
| | - Kindie Tesfaye
- Sustianable Agrifood Systems Program, International Maize and Wheat Improvement Centre (CIMMYT), Addis Ababa, Ethiopia
| | - MacDonald B. Jumbo
- Crop Improvement Program, International Crops Research Institute for Semi-Arid Tropics, Bamako, Mali
| | - Biswanath Das
- Global Maize Program, International Maize and Wheat Improvement Centre (CIMMYT), Nairobi, Kenya
| | - Egas J. Nhamucho
- Instituto de Investigação Agrária de Moçambique (IIAM), Chokwe, Mozambique
| | | | | | | | - Ndhlela Thokozile
- Global Maize Program, International Maize and Wheat Improvement Centre (CIMMYT), Harare, Zimbabwe
| | - Xavier Mhike
- Global Maize Program, International Maize and Wheat Improvement Centre (CIMMYT), Harare, Zimbabwe
| | - Boddupalli M. Prasanna
- Global Maize Program, International Maize and Wheat Improvement Centre (CIMMYT), Nairobi, Kenya
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Njeru F, Wambua A, Muge E, Haesaert G, Gettemans J, Misinzo G. Major biotic stresses affecting maize production in Kenya and their implications for food security. PeerJ 2023; 11:e15685. [PMID: 38050609 PMCID: PMC10693822 DOI: 10.7717/peerj.15685] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Accepted: 06/14/2023] [Indexed: 12/06/2023] Open
Abstract
Maize (Zea mays L.) is a staple food for many households in sub-Saharan Africa (SSA) and also contributes to the gross domestic product (GDP). However, the maize yields reported in most SSA countries are very low and this is mainly attributed to biotic and abiotic stresses. These stresses have been exacerbated by climate change which has led to long periods of drought or heavy flooding and the emergence of new biotic stresses. Few reports exist which compile the biotic stresses affecting maize production in SSA. Here, five major biotic stresses of maize in Kenya are presented which are attributed to high yield losses. They include Maize lethal necrosis, fall armyworm, gray leaf spot, turcicum leaf blight and desert locusts. Maize lethal necrosis and fall armyworm are new biotic stresses to the Kenyan maize farmer while gray leaf spot, and turcicum leaf blight are endemic to the region. The invasion by the desert locusts is speculated to be caused by climate change. The biotic stresses cause a reduction in maize yield of 30-100% threatening food security. Therefore, this review focuses on the cause, control measures employed to control these diseases and future prospective. There should be deliberate efforts from the government and researchers to control biotic stresses affecting maize yields as the effect of these stresses is being exacerbated by the changing climate.
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Affiliation(s)
- Faith Njeru
- SACIDS Africa Centre of Excellence for Infectious Diseases, SACIDS Foundation for One Health, Sokoine University of Agriculture, Morogoro, Tanzania
- Department of Veterinary Microbiology, Parasitology and Biotechnology, College of Veterinary Medicines and Biomedical Sciences, Sokoine University of Agriculture, Morogoro, Tanzania
| | - Angeline Wambua
- Department of Physical Sciences, Chuka University, Chuka, Kenya
| | - Edward Muge
- Department of Biochemistry, University of Nairobi, Nairobi, Kenya
| | - Geert Haesaert
- Department of Plants and Crops, Ghent University, Ghent, Belgium
| | - Jan Gettemans
- Department of Biomolecular Medicine, Ghent University, Ghent, Belgium
| | - Gerald Misinzo
- SACIDS Africa Centre of Excellence for Infectious Diseases, SACIDS Foundation for One Health, Sokoine University of Agriculture, Morogoro, Tanzania
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Krause MD, Piepho HP, Dias KOG, Singh AK, Beavis WD. Models to estimate genetic gain of soybean seed yield from annual multi-environment field trials. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2023; 136:252. [PMID: 37987845 PMCID: PMC10663270 DOI: 10.1007/s00122-023-04470-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Accepted: 09/25/2023] [Indexed: 11/22/2023]
Abstract
KEY MESSAGE Simulations demonstrated that estimates of realized genetic gain from linear mixed models using regional trials are biased to some degree. Thus, we recommend multiple selected models to obtain a range of reasonable estimates. Genetic improvements of discrete characteristics are obvious and easy to demonstrate, while quantitative traits require reliable and accurate methods to disentangle the confounding genetic and non-genetic components. Stochastic simulations of soybean [Glycine max (L.) Merr.] breeding programs were performed to evaluate linear mixed models to estimate the realized genetic gain (RGG) from annual multi-environment trials (MET). True breeding values were simulated under an infinitesimal model to represent the genetic contributions to soybean seed yield under various MET conditions. Estimators were evaluated using objective criteria of bias and linearity. Covariance modeling and direct versus indirect estimation-based models resulted in a substantial range of estimated values, all of which were biased to some degree. Although no models produced unbiased estimates, the three best-performing models resulted in an average bias of [Formula: see text] kg/ha[Formula: see text]/yr[Formula: see text] ([Formula: see text] bu/ac[Formula: see text]/yr[Formula: see text]). Rather than relying on a single model to estimate RGG, we recommend the application of several models with minimal and directional bias. Further, based on the parameters used in the simulations, we do not think it is appropriate to use any single model to compare breeding programs or quantify the efficiency of proposed new breeding strategies. Lastly, for public soybean programs breeding for maturity groups II and III in North America, the estimated RGG values ranged from 18.16 to 39.68 kg/ha[Formula: see text]/yr[Formula: see text] (0.27-0.59 bu/ac[Formula: see text]/yr[Formula: see text]) from 1989 to 2019. These results provide strong evidence that public breeders have significantly improved soybean germplasm for seed yield in the primary production areas of North America.
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Affiliation(s)
| | | | - Kaio O G Dias
- Department of General Biology, Federal University of Viçosa, Viçosa, Brazil
| | - Asheesh K Singh
- Department of Agronomy, Iowa State University, Ames, IA, USA
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Dallinger HG, Löschenberger F, Bistrich H, Ametz C, Hetzendorfer H, Morales L, Michel S, Buerstmayr H. Predictor bias in genomic and phenomic selection. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2023; 136:235. [PMID: 37878079 PMCID: PMC10600307 DOI: 10.1007/s00122-023-04479-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Accepted: 09/08/2023] [Indexed: 10/26/2023]
Abstract
KEY MESSAGE NIRS of wheat grains as phenomic predictors for grain yield show inflated prediction ability and are biased toward grain protein content. Estimating the breeding value of individuals using genome-wide marker data (genomic prediction) is currently one of the most important drivers of breeding progress in major crops. Recently, phenomic technologies, including remote sensing and aerial hyperspectral imaging of plant canopies, have made it feasible to predict the breeding value of individuals in the absence of genetic marker data. This is commonly referred to as phenomic prediction. Hyperspectral measurements in the form of near-infrared spectroscopy have been used since the 1980 s to predict compositional parameters of harvest products. Moreover, in recent studies NIRS from grains was used to predict grain yield. The same studies showed that phenomic prediction can outperform genomic prediction for grain yield. The genome is static and not environment dependent, thereby limiting genomic prediction ability. Gene expression is tissue specific and differs under environmental influences, leading to a tissue- and environment-specific phenome, potentially explaining the higher predictive ability of phenomic prediction. Here, we compare genomic prediction and phenomic prediction from hyperspectral measurements of wheat grains for the prediction of a variety of traits including grain yield. We show that phenomic predictions outperform genomic prediction for some traits. However, phenomic predictions are biased toward the information present in the predictor. Future studies on this topic should investigate whether population parameters are retained in phenomic prediction as they are in genomic prediction. Furthermore, we find that unbiased phenomic prediction abilities are considerably lower than previously reported and recommend a method to circumvent this issue.
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Affiliation(s)
- Hermann Gregor Dallinger
- Institute of Biotechnology in Plant Production, Department of Agrobiotechnology, IFA-Tulln, University of Natural Resources and Life Sciences Vienna, Konrad-Lorenz-Str. 20, 3430, Tulln, Austria.
- Saatzucht Donau GesmbH & Co KG, Saatzuchtstrasse 11, 2301, Probstdorf, Austria.
| | | | - Herbert Bistrich
- Saatzucht Donau GesmbH & Co KG, Saatzuchtstrasse 11, 2301, Probstdorf, Austria
| | - Christian Ametz
- Saatzucht Donau GesmbH & Co KG, Saatzuchtstrasse 11, 2301, Probstdorf, Austria
| | | | - Laura Morales
- Institute of Biotechnology in Plant Production, Department of Agrobiotechnology, IFA-Tulln, University of Natural Resources and Life Sciences Vienna, Konrad-Lorenz-Str. 20, 3430, Tulln, Austria
| | - Sebastian Michel
- Institute of Biotechnology in Plant Production, Department of Agrobiotechnology, IFA-Tulln, University of Natural Resources and Life Sciences Vienna, Konrad-Lorenz-Str. 20, 3430, Tulln, Austria
| | - Hermann Buerstmayr
- Institute of Biotechnology in Plant Production, Department of Agrobiotechnology, IFA-Tulln, University of Natural Resources and Life Sciences Vienna, Konrad-Lorenz-Str. 20, 3430, Tulln, Austria
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Pixley KV, Cairns JE, Lopez-Ridaura S, Ojiewo CO, Dawud MA, Drabo I, Mindaye T, Nebie B, Asea G, Das B, Daudi H, Desmae H, Batieno BJ, Boukar O, Mukankusi CTM, Nkalubo ST, Hearne SJ, Dhugga KS, Gandhi H, Snapp S, Zepeda-Villarreal EA. Redesigning crop varieties to win the race between climate change and food security. MOLECULAR PLANT 2023; 16:1590-1611. [PMID: 37674314 DOI: 10.1016/j.molp.2023.09.003] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/04/2023] [Revised: 08/17/2023] [Accepted: 09/03/2023] [Indexed: 09/08/2023]
Abstract
Climate change poses daunting challenges to agricultural production and food security. Rising temperatures, shifting weather patterns, and more frequent extreme events have already demonstrated their effects on local, regional, and global agricultural systems. Crop varieties that withstand climate-related stresses and are suitable for cultivation in innovative cropping systems will be crucial to maximize risk avoidance, productivity, and profitability under climate-changed environments. We surveyed 588 expert stakeholders to predict current and novel traits that may be essential for future pearl millet, sorghum, maize, groundnut, cowpea, and common bean varieties, particularly in sub-Saharan Africa. We then review the current progress and prospects for breeding three prioritized future-essential traits for each of these crops. Experts predict that most current breeding priorities will remain important, but that rates of genetic gain must increase to keep pace with climate challenges and consumer demands. Importantly, the predicted future-essential traits include innovative breeding targets that must also be prioritized; for example, (1) optimized rhizosphere microbiome, with benefits for P, N, and water use efficiency, (2) optimized performance across or in specific cropping systems, (3) lower nighttime respiration, (4) improved stover quality, and (5) increased early vigor. We further discuss cutting-edge tools and approaches to discover, validate, and incorporate novel genetic diversity from exotic germplasm into breeding populations with unprecedented precision, accuracy, and speed. We conclude that the greatest challenge to developing crop varieties to win the race between climate change and food security might be our innovativeness in defining and boldness to breed for the traits of tomorrow.
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Affiliation(s)
- Kevin V Pixley
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico.
| | - Jill E Cairns
- International Maize and Wheat Improvement Center (CIMMYT), Harare, Zimbabwe
| | | | - Chris O Ojiewo
- International Maize and Wheat Improvement Center (CIMMYT), Nairobi, Kenya
| | | | - Inoussa Drabo
- International Maize and Wheat Improvement Center (CIMMYT), Dakar, Senegal
| | - Taye Mindaye
- Ethiopian Institute of Agricultural Research (EIAR), Addis Ababa, Ethiopia
| | - Baloua Nebie
- International Maize and Wheat Improvement Center (CIMMYT), Dakar, Senegal
| | - Godfrey Asea
- National Agricultural Research Organization (NARO), Kampala, Uganda
| | - Biswanath Das
- International Maize and Wheat Improvement Center (CIMMYT), Nairobi, Kenya
| | - Happy Daudi
- Tanzania Agricultural Research Institute (TARI), Naliendele, Tanzania
| | - Haile Desmae
- International Maize and Wheat Improvement Center (CIMMYT), Dakar, Senegal
| | - Benoit Joseph Batieno
- Institut de l'Environnement et de Recherches Agricoles (INERA), Ouagadougou, Burkina Faso
| | - Ousmane Boukar
- International Institute of Tropicl Agriculture (IITA), Kano, Nigeria
| | | | | | - Sarah J Hearne
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Kanwarpal S Dhugga
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Harish Gandhi
- International Maize and Wheat Improvement Center (CIMMYT), Nairobi, Kenya
| | - Sieglinde Snapp
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
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Asea G, Kwemoi DB, Sneller C, Kasozi CL, Das B, Musundire L, Makumbi D, Beyene Y, Prasanna BM. Genetic trends for yield and key agronomic traits in pre-commercial and commercial maize varieties between 2008 and 2020 in Uganda. FRONTIERS IN PLANT SCIENCE 2023; 14:1020667. [PMID: 36968404 PMCID: PMC10036907 DOI: 10.3389/fpls.2023.1020667] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/16/2022] [Accepted: 02/22/2023] [Indexed: 06/18/2023]
Abstract
Estimating genetic gains is vital to optimize breeding programs for increased efficiency. Genetic gains should translate into productivity gains if returns to investments in breeding and impact are to be realized. The objective of this study was to estimate genetic gain for grain yield and key agronomic traits in pre-commercial and commercial maize varieties from public and private breeding programs tested in (i) national performance trials (NPT), (ii) era trial and, (iii) compare the trends with the national average. The study used (i) historical NPT data on 419 improved maize varieties evaluated in 23 trials at 6-8 locations each between 2008 and 2020, and (ii) data from an era trial of 54 maize hybrids released between 1999 and 2020. The NPT data was first analyzed using a mixed model and resulting estimate for each entry was regressed onto its first year of testing. Analysis was done over all entries, only entries from National Agricultural Research Organization (NARO), International Maize and Wheat Improvement Center (CIMMYT), or private seed companies. Estimated genetic gain was 2.25% or 81 kg ha-1 year-1 from the NPT analysis. A comparison of genetic trends by source indicated that CIMMYT entries had a gain of 1.98% year-1 or 106 kg ha-1 year-1. In contrast, NARO and private sector maize entries recorded genetic gains of 1.30% year-1 (59 kg ha-1 year-1) and 1.71% year-1 (79 kg ha-1 year-1), respectively. Varieties from NARO and private sector showed comparable mean yields of 4.56 t ha-1 and 4.62 t ha-1, respectively, while hybrids from CIMMYT had a mean of 5.37 t ha-1. Era analysis indicated significant genetic gain of 1.69% year-1 or 55 kg ha-1 year-1, while a significant national productivity gain of 1.48% year-1 (37 kg ha-1 year-1) was obtained. The study, thus, demonstrated the importance of public-private partnerships in development and delivery of new genetics to farmers in Uganda.
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Affiliation(s)
- Godfrey Asea
- National Crops Resources Research Institute, National Agricultural Research Organization, Kampala, Uganda
| | - Daniel Bomet Kwemoi
- National Crops Resources Research Institute, National Agricultural Research Organization, Kampala, Uganda
| | - Clay Sneller
- Department of Horticulture and Crop Science, The Ohio State University, Wooster, OH, United States
| | - Charles L. Kasozi
- National Crops Resources Research Institute, National Agricultural Research Organization, Kampala, Uganda
| | - Biswanath Das
- Global Maize Program, International Maize and Wheat Improvement Center (CIMMYT), Nairobi, Kenya
| | - Lennin Musundire
- Global Maize Program, International Maize and Wheat Improvement Center (CIMMYT), Nairobi, Kenya
| | - Dan Makumbi
- Global Maize Program, International Maize and Wheat Improvement Center (CIMMYT), Nairobi, Kenya
| | - Yoseph Beyene
- Global Maize Program, International Maize and Wheat Improvement Center (CIMMYT), Nairobi, Kenya
| | - Boddupalli M. Prasanna
- Global Maize Program, International Maize and Wheat Improvement Center (CIMMYT), Nairobi, Kenya
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