Finger Millet Output Commercialization Among Smallholder Farmers: Role of Agricultural Innovations in Kenya

Finger millet has been an alternative form of sustenance for resource-poor farmers, especially in arid and semi-arid areas in Kenya. However, lack of innovational improvement has often locked small producers into subsistence production and less commercialization. As a result, integration of smallholder farmers into finger millet output markets is still limited. Recently, research and development organizations facilitated the development of new innovations and market linkages for finger millet and other traditional crops for marginal areas in Kenya. But, little is known about the role of these innovations on finger millet commercialization. This study, therefore, sought to determine the level and factors that influence finger millet commercialization in the rise of innovation promotion. Multi-stage sampling technique was used to select a total of 384 smallholder finger millet farmers from Elgeyo-Marakwet County, Kenya. The household commercialization index was used to assess the degree of commercialization, while the double hurdle model was used to determine factors that influence market participation and intensity of participation. The mean household commercialization index was 0.33. The results of the study indicate that education, finger millet yield, finger millet crop area, contact with extension officers, integrated pest and weed management, improved finger millet variety, off/non-farm income and membership to finger millet group marketing were the major determinants of market participation. The study found out that many smallholder finger millet farmers are subsistence oriented. Thus, the study recommends that innovations that help farmers reduce market transaction cost could be promoted alongside yield-enhancing innovations to facilitate farmers' participation in output markets hence increased incomes and food security. Keywords: Agricultural innovations, output commercialization, finger millet, smallholde

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

High-resolution linkage map and chromosome-scale genome assembly for cassava (Manihot esculenta Crantz) from 10 populations

Cassava (Manihot esculenta Crantz) is a major staple crop in Africa, Asia, and South America, and its starchy roots provide nourishment for 800 million people worldwide. Although native to South America, cassava was brought to Africa 400–500 years ago and is now widely cultivated across sub-Saharan Africa, but it is subject to biotic and abiotic stresses. To assist in the rapid identification of markers for pathogen resistance and crop traits, and to accelerate breeding programs, we generated a framework map for M. esculenta Crantz from reduced representation sequencing [genotyping-by-sequencing (GBS)]. The composite 2412-cM map integrates 10 biparental maps (comprising 3480 meioses) and organizes 22,403 genetic markers on 18 chromosomes, in agreement with the observed karyotype. We used the map to anchor 71.9% of the draft genome assembly and 90.7% of the predicted protein-coding genes. The chromosome-anchored genome sequence will be useful for breeding improvement by assisting in the rapid identification of markers linked to important traits, and in providing a framework for genomic selectionenhanced breeding of this important crop.Bill and Melinda Gates Foundation (BMGF) Grant OPPGD1493. University of Arizona. CGIAR Research Program on Roots, Tubers, and Bananas. Next Generation Cassava Breeding grant OPP1048542 from BMGF and the United Kingdom Department for International Development. BMGF grant OPPGD1016 to IITA. National Institutes of Health S10 Instrumentation Grants S10RR029668 and S10RR027303.http://www.g3journal.orghb201

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