Person: Singh, R.G.
Loading...
Email Address
Birth Date
Research Projects
Organizational Units
Job Title
Last Name
Singh
First Name
R.G.
Name
Singh, R.G.
ORCID ID
0000-0002-3153-688210 results
Search Results
Now showing 1 - 10 of 10
- Assessing sustainability in smallholder vegetable farms in Benin Republic: A matrix approach(Elsevier B.V., 2024) Fassinou Hotegni, V.N.; Nouhougan Alexandre Guidimadjègbè; Ayenan, M.A.T.; Singh, R.G.; Odjo, S.
Publication - Weed management and tillage effect on rainfed maize production in three agro-ecologies in Mexico(Wiley, 2022) Fonteyne, S.; Leal González, A.J.; Osorio Alcalá, L.; Villa Alcántara, J.; Santos, C.; Nuñez, O.; Ovando Galdámez, J.R.; Singh, R.G.; Verhulst, N.
Publication - Disaggregating the value of conservation agriculture to inform smallholder transition to sustainable farming: a mexican case study(MDPI, 2021) Monjardino, M.; Lopez-Ridaura, S.; Van Loon, J.; Mottaleb, K.A.; Kruseman, G.; Zepeda Villarreal, E.A.; Ortiz Hernández, E.; Burgueño, J.; Singh, R.G.; Govaerts, B.; Erenstein, O.
Publication - One CGIAR and the Integrated Agri-food Systems Initiative: from short-termism to transformation of the world's food systems(Public Library of Science, 2021) Govaerts, B.; Negra, C.; Camacho Villa, T.C.; Chavez, X.; Diaz Espinosa, A.; Fonteyne, S.; Gardeazabal, A.; González, G.; Singh, R.G.; Kommerell, V.; Kropff, W.; Lopez-Saavedra, V.; Mena-Lopez, G.; Odjo, S.; Palacios-Rojas, N.; Ramirez-Villegas, J.; Van Loon, J.; Vega, D.; Verhulst, N.; Woltering, L.; Jahn, M.; Kropff, Martinus
Publication - Rotation, mulch and zero tillage reduce weeds in a long-term conservation agriculture trial(MDPI, 2020) Fonteyne, S.; Singh, R.G.; Govaerts, B.; Verhulst, N.
Publication - Prediction of multiple-trait and multiple-environment genomic data using recommender systems(Genetics Society of America, 2018) Montesinos-Lopez, O.A.; Montesinos-López, A.; Crossa, J.; Montesinos-Lopez, J.C.; Mota-Sanchez, D.; Estrada-González, F.; Gillberg, J.; Singh, R.G.; Mondal, S.; Juliana, P.In genomic-enabled prediction, the task of improving the accuracy of the prediction of lines in environments is difficult because the available information is generally sparse and usually has low correlations between traits. In current genomic selection, although researchers have a large amount of information and appropriate statistical models to process it, there is still limited computing efficiency to do so. Although some statistical models are usually mathematically elegant, many of them are also computationally inefficient, and they are impractical for many traits, lines, environments, and years because they need to sample from huge normal multivariate distributions. For these reasons, this study explores two recommender systems: itembased collaborative filtering (IBCF) and the matrix factorization algorithm (MF) in the context of multiple traits and multiple environments. The IBCF and MF methods were compared with two conventional methods on simulated and real data. Results of the simulated and real data sets show that the IBCF technique was slightly better in terms of prediction accuracy than the two conventional methods and the MF method when the correlation was moderately high. The IBCF technique is very attractive because it produces good predictions when there is high correlation between items (environment–trait combinations) and its implementation is computationally feasible, which can be useful for plant breeders who deal with very large data sets.
Publication - Soil organic carbon changes after seven years of conservation agriculture in a rice–wheat system of the eastern Indo-Gangetic Plains(Wiley, 2017) Sapkota, T.; Jat, R.K.; Singh, R.G.; Jat, M.L.; Stirling, C.; Jat, M.K.; Bijarniya, D.; Kumar, M.; Yadvinder-Singh; Saharawat, Y.S.; Gupta, R.K.Sequestration of soil organic carbon (SOC) is an important strategy to improve soil quality and to mitigate climate change. To investigate changes in SOC under conservation agriculture (CA), we measured SOC concentrations after seven years of rice (Oryza sativa L.)–wheat (Triticum aestivum L.) rotations in the eastern Indo-Gangetic Plains (IGP) of India under various combinations of tillage and crop establishment methods. The six treatments were as follows: conventional till transplanted rice followed by conventional till wheat (CTR-CTW), CTR followed by zero-till wheat (CTR-ZTW), ZT direct-seeded rice followed by CTW (ZTDSR-CTW), ZTDSR followed by ZT wheat both on permanent raised beds with residue (PBDSR-PBW+R), and ZTDSR followed by ZTW both with (ZTDSR-ZTW+R) and without residues (ZTDSR-ZTW). We hypothesized that CA systems (i.e. ZT with residue retention) would sequester more carbon (C) than CT. After seven years, ZTDSRZTW+ R and PBDSR-PBW+R increased SOC at 0–0.6 m depth by 4.7 and 3.0 t C/ha, respectively, whereas the CTR-CTW system resulted in a decrease in SOC of 0.9 t C/ha. Over the same soil depth, ZT without residue retention (ZTDSR-ZTW) only increased SOC by 1.1 t C/ha. There was no increase in SOC where ZT in either rice or wheat was followed by CT in the next crop (i.e. CTRZTW and ZTDSR-CTW), most likely because the benefit of ZT is lost when followed by tillage. Tillage and crop establishment methods had no significant effect on the SOC stock below the 0.15-m soil layer. Over the seven years, the total carbon input from above-ground residues was ca. 14.5 t/ha in ZTDSR-ZTW+R and PBDSR-PBW+R, almost sixfold greater than in the other systems. Our findings suggest that the increased biomass production achieved through a combination of ZT and partial residue retention offers an opportunity to increase SOC whilst allowing residues to be used for other purposes.
Publication - Wheat quality improvement at CIMMYT and the use of genomic selection on it(Elsevier, 2016) Guzman, C.; Peña, Roberto; Singh, R.G.; Autrique, E.; Dreisigacker, S.; Crossa, J.; Rutkoski, J.; Poland, J.; Battenfield, S.D.The International Center for Maize and Wheat Improvement (CIMMYT) leads the Global Wheat Program, whose main objective is to increase the productivity of wheat cropping systems to reduce poverty in developing countries. The priorities of the program are high grain yield, disease resistance, tolerance to abiotic stresses (drought and heat), and desirable quality. The Wheat Chemistry and Quality Laboratory has been continuously evolving to be able to analyze the largest number of samples possible, in the shortest time, at lowest cost, in order to deliver data on diverse quality traits on time to the breeders for making selections for advancement in the breeding pipeline. The participation of wheat quality analysis/selection is carried out in two stages of the breeding process: evaluation of the parental lines for new crosses and advanced lines in preliminary and elite yield trials. Thousands of lines are analyzed which requires a big investment in resources. Genomic selection has been proposed to assist in selecting for quality and other traits in breeding programs. Genomic selection can predict quantitative traits and is applicable to multiple quantitative traits in a breeding pipeline by attaining historical phenotypes and adding high-density genotypic information. Due to advances in sequencing technology, genome-wide single nucleotide polymorphism markers are available through genotyping-by-sequencing at a cost conducive to application for genomic selection. At CIMMYT, genomic selection has been applied to predict all of the processing and end-use quality traits regularly tested in the spring wheat breeding program. These traits have variable levels of prediction accuracy, however, they demonstrated that most expensive traits, dough rheology and baking final product, can be predicted with a high degree of confidence. Currently it is being explored how to combine both phenotypic and genomic selection to make more efficient the genetic improvement for quality traits at CIMMYT spring wheat breeding program.
Publication - Canopy temperature and vegetation indices from high-throughput phenotyping improve accuracy of pedigree and genomic selection for grain yield in wheat(Genetics Society of America, 2016) Rutkoski, J.; Poland, J.; Mondal, S.; Autrique, E.; González Pérez, L.; Crossa, J.; Reynolds, M.P.; Singh, R.G.Genomic selection can be applied prior to phenotyping, enabling shorter breeding cycles and greater rates of genetic gain relative to phenotypic selection. Traits measured using high-throughput phenotyping based on proximal or remote sensing could be useful for improving pedigree and genomic prediction model accuracies for traits not yet possible to phenotype directly. We tested if using aerial measurements of canopy temperature, and green and red normalized difference vegetation index as secondary traits in pedigree and genomic best linear unbiased prediction models could increase accuracy for grain yield in wheat, Triticum aestivum L., using 557 lines in five environments. Secondary traits on training and test sets, and grain yield on the training set were modeled as multivariate, and compared to univariate models with grain yield on the training set only. Cross validation accuracies were estimated within and across-environment, with and without replication, and with and without correcting for days to heading. We observed that, within environment, with unreplicated secondary trait data, and without correcting for days to heading, secondary traits increased accuracies for grain yield by 56% in pedigree, and 70% in genomic prediction models, on average. Secondary traits increased accuracy slightly more when replicated, and considerably less when models corrected for days to heading. In across-environment prediction, trends were similar but less consistent. These results show that secondary traits measured in high-throughput could be used in pedigree and genomic prediction to improve accuracy. This approach could improve selection in wheat during early stages if validated in early-generation breeding plots.
Publication - Reaching out to farmers with high zinc wheat varieties through public-private partnerships: an experience from eastern-gangetic plains of India(Openventio, 2015) Velu, G.; Singh, R.G.; Balasubramaniam, A.; Mishra, V.K.; Chand, R.; Chhavi Tiwari; Joshi, A.K.; Parminder Virk; Binu Cherian; Pfeiffer, W.The main objective of the HarvestPlus led wheat biofortification breeding program at the International Maize and Wheat Improvement Center (CIMMYT) and its national program partners in South Asia is to develop and disseminate competitive wheat varieties with high grain zinc (Zn) and other essential agronomic features. The emphasis of this program is to introduce novel sources of genetic diversity from wild species and landraces, into the adapted wheat background. This variation is being exploited through limited backcross approach with shuttle breeding at two contrasting locations in Mexico, which resulted in widely adapted, durable rust and foliar disease resistant, high Zn wheat varieties. The new wheat varieties developed by CIMMYT in HarvestPlus project are 20-40% superior in grain Zn concentration and are agronomically at par or superior to the popular wheat cultivars of South Asia. The biofortification breeding program of CIMMYT utilizes new wheat varieties from the core-breeding program as background parents that are higher yielding, resistant to rusts, heat tolerant, wateruse efficient and 5-10% higher yielding than main varieties grown at present. The biofortified high Zn wheat varieties with 20 to 40% (8-12 mg/kg) Zn superiority and grain yield potential at par or superior to the popular wheat varieties are being adopted by small-holder farmers in South Asia. Through Public-private partnerships (PPP) more than 50,000 farmers and 250,000 household members expected to benefit from the Zn-biofortified wheat varieties in South Asia by the 2015-2016 wheat seasons.
Publication