Crop conceptual model for predicting productivity of bread wheat in semi-arid Kenya

 P. K. Kimurto1, K. Gottschalk2, M. G. Kinyua3, J. B. O. Ogola4, B. K. Towett 1(1. Department of Crops, Horticulture & Soil Sciences, Egerton University, P.O. Box 536, Njoro, Kenya;2. Leibniz-Institut für Agrartechnik Potsdam-Bornim e.V. ATB, Max-Eyth-Allee 100, 14469 Potsdam, Germany;3. Department of Plant Breeding and Biotechnology, Moi University, P.O. Box 39000, Eldoret, Kenya;4. Department of Plant Production, University of Venda, a, Private bag X5050, South Africa) Abstract: Carrying out field trial-research in dryland areas is usually expensive and costly for most national breeding programmes; hence development of simple crop simulation models for predicting crop performance in actual semi-arid and arid lands (ASALS) would reduce the number of field evaluation trials.  This is especially critical in developing countries like Kenya where dry areas is approximately 83% of total land area and annual rainfall in these area is low, unreliable and highly erratic, causing frequent crop failures, food insecurity and famine.  This paper used data generated from the rain shelter by measurement of evapotranspiration together with weather variables in Katumani to predict wheat yields in that site.  Maximum yield of the wheat genotype considered for genotype Chozi under ideal conditions was 5 t/ha.  Total above-ground biomass was obtained and grain yield was to be predicted by the model.  Transpiration was estimated from the relationship between total dry matter production and normalised TE (7.8 Pa).  The results presented are based on the assumption that all agronomic conditions were optimal and drought stress was the major limiting factor.  Predicted grain yield obtained from the conceptual model compares very well with realised yields from actual field experiments with variances of 14% – 43% depending on watering regime.  This study showed that it is possible to develop simple conceptual model to predict productivity in wheat in semi-arid areas of Kenya to supplement complicated and more sophisticated models like CERES-maize and ECHAM models earlier used in Kenya.  The presence of uncontrolled factors in the simulation not accounted for in the estimation and could have contributed to decrease in observed yield need to be included in the model, hence modulation of the equations by introducing these factors may be necessary to reduce variances; thus need to be quantified.  To improve the accuracy of prediction and increase wheat production in these areas measures that conserve water and/or make more water available to the crop such as prevention or minimisation of run-off, and rain water harvesting for supplemental irrigation are necessary.Keywords: wheat, conceptual model, drought, evapotranspiration, yield response Citation: Kimurto P. K., K. Gottschalk, M. G. Kinyua, J. B. O. Ogola, and B. K. Towett.  Crop conceptual model for predicting productivity of bread wheat in semi-arid Kenya.  Agric Eng Int: CIGR Journal, 2010, 12(3): 25-37.&nbsp

Evaluation of chickpea genotypes for resistance to Ascochyta blight (Ascochyta rabiei) disease in the dry highlands of Kenya

Chickpea (Cicer arietinum) is an edible legume grown widely for its nutritious seed, which is rich in protein, minerals, vitamins and dietary fibre. It’s a new crop in Kenya whose potential has not been utilized fully due to abiotic and biotic stresses that limit its productivity. The crop is affected mainly by Ascochyta blight (AB) which is widespread in cool dry highlands causing up to 100% yield loss. The objective of this study was to evalu- ate the resistance of selected chickpea genotypes to AB in dry highlands of Kenya. The study was done in 2 sites (Egerton University-Njoro) and Agricultural Training centre-ATC-Koibatek) for one season during long rains of 2010/2011 growing season. Thirty six genotypes from reference sets and mini-core samples introduced from ICR- SAT were evaluated. There were significant (P<0.001) differences in AB responses and grain yield performance in test genotypes in both sites. AB was more severe at Egerton-Njoro (mean score 5.7) than ATC-Koibatek (mean score 4.25), with subsequent low grain yield. Genotypes ICC7052, ICC4463, ICC4363, ICC2884, ICC7150, ICC15294 and ICC11627 had both highest grain yield in decreasing order (mean range 1790-1053 Kg ha-1) and best resist- ance to AB. Further evaluation is needed in other multi-locations and their use in breeding program determined especially because of their undesirable black seed color. Commercial varieties (LDT068, LDT065, Chania desi 1, and Saina K1) were all susceptible to AB, but with grain yield >1200 Kg ha-1. The findings of the study showed that chickpea should be sown during the short rains (summer) in the dry highlands of Kenya when conditions are drier and warmer and less favorable for AB infection. However yield could be increased by shifting the sowing date from dry season to long rain (winter) thus avoiding terminal drought if AB resistant cultivars with acceptable agronomic traits could be identified

Polymorphism information content (PIC) value range of 1,616 PKAM screened over 184 <i>Cajanus</i> accessions.

<p>Polymorphism information content (PIC) value range of 1,616 PKAM screened over 184 <i>Cajanus</i> accessions.</p

Diversity in three different gene pools (GP) of pigeonpea germplasm.

<p>Na  =  No. of Different Alleles, Ne  =  No. of Effective Alleles  =  1 / (Sum pî2), I  =  Shannon's Information Index  =  −1* Sum (pi * Ln (pi)), Ho  =  Observed Heterozygosity  =  No. of Hets / N, He  =  Expected Heterozygosity  =  1 - Sum pî2, UHe  =  Unbiased Expected Heterozygosity  =  (2N / (2N-1)) * He, F  =  Fixation Index  =  (He − Ho) / He  =  1 − (Ho / He) (Where pi is the frequency of the ith allele for the population & Sum pî2 is the sum of the squared population allele frequencies), %P =  percent of loci polymorphic.</p

Gene diversity across breeding lines, landraces and wild relatives estimated by the 875 mapped PKAM.

<p>Gene diversity across breeding lines, landraces and wild relatives estimated by the 875 mapped PKAM.</p

Geographical distribution of the collection sites for cultivated and wild <i>Cajanus</i> accessions.

<p>Geographical distribution of the collection sites for cultivated and wild <i>Cajanus</i> accessions.</p

Neighbor-joinging tree of pairwise relatedness among 184 accessions.

<p>Neighbor-joinging tree of pairwise relatedness among 184 accessions.</p

SNP marker polymorphism status across cultivated and wild <i>Cajanus</i> accessions.

<p>SNP marker polymorphism status across cultivated and wild <i>Cajanus</i> accessions.</p

Population analysis of <i>Cajanus</i> accessions present in Indian regions and provinces

<p><i>a)</i> Principal coordinates analysis of domesticated pigeonpea and wild relatives in 11 defined zones <i>b)</i> Analysis of molecular variance (AMOVA) in 11 defined zones <i>c)</i> Structure results across gene pools at the province scale</p

Translational genomics in agriculture: Some examples in grain legumes

Recent advances in genomics and associated disciplines like bioinformatics have made it possible to develop genomic resources, such as large-scale sequence data for any crop species. While these datasets have been proven very useful for the understanding of genome architecture and dynamics as well as facilitating the discovery of genes, an obligation for, and challenge to the scientific community is to translate genome information to develop products, i.e. superior lines for trait(s) of interest. We call this approach, “translational genomics in agriculture” (TGA). TGA is currently in practice for cereal crops, such as maize (Zea mays) and rice (Oryza sativa), mainly in developed countries and by the private sector; progress has been slow for legume crops. Grown globally on 62.8 million ha with a production of 53.2 million tons and a value of nearly 24.2 billion dollars, the majority of these legumes have low crop productivity (<1 ton/ hectare) and are in the developing countries of sub Saharan Africa, Asia and South America. Interestingly, the last five years have seen enormous progress in genomics for these legume crops. Therefore, it is time to implement TGA in legume crops in order to enhance crop productivity and to ensure food security in developing countries. Prospects, as well as some success stories of TGA, in addition to advances in genomics, trait mapping and gene expression analysis are discussed for five leading legume crops, chickpea (Cicer arietinum), common bean (Phaseolus vulgaris), groundnut (Arachis hypogaea), pigeonpea (Cajanus cajan) and soybean (Glycine max). Some efforts have also been outlined to initiate/ accelerate TGA in three additional legume crops namely faba bean (Vicia faba), lentil (Lens culinaris) and pea (Pisum sativum)