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

Study of root traits of chickpea (Cicer arietinum L.) under drought stress

Roots are among the first defence towards drought with other morpho-physiological and biochemical mechanisms employed by plants. To understand precisely the root traits contribution towards yield, parental chickpea genotypes with well known drought response were field evaluated under drought and optimal irrigation in rain-out shelter. A total of ten genotypes planted in 1.2 m PVC lysimeters were subjected to three water stress levels: high moisture stress, medium water stress, and low water stresses. Root traits, such as root length density, total root dry weight, root dry weight and root: shoot ratio, were measured at 40 days after sowing. The roots were washed and scanned using WinRHIZO software. The ANOVA showed that there was significant difference (P < 0.05) in traits measured amongst test genotypes which included shoot biomass, root biomass, total root length (RL) and root length density (RLD). The results also showed that there were significant variations (P < 0.05) in water regimes and traits decreased with increasing moisture stress from low to high moisture regime. Furthermore, there were variations in root anatomy between the two major chickpea types where majority of the best performing genotypes under low moisture regimes were of the Desi type (e.g. ICC 4958, ICCV 00108, ICCV 92944 and ICCV 92318) as compared to Kabulis which had better and higher response under high moisture regime in this study. These traits could be used for indirect selection for drought tolerance especially in early stages of breeding for drought tolerance which would consequently reduce the cost of multi-location field evaluation in the breeding programs

Portable X-ray fluorescence (pXRF) calibration for analysis of nutrient concentrations and trace element contaminants in fertilisers

With the increasing popularity of local blending of fertilisers, the fertiliser industry faces issues regarding quality control and fertiliser adulteration. Another problem is the contamination of fertilisers with trace elements that have been shown to subsequently accumulate in the soil and be taken up by plants, posing a danger to the environment and human health. Conventional characterisation methods necessary to ensure the quality of fertilisers and to comply with local regulations are costly, time consuming and sometimes not even accessible. Alternatively, using a wide range of unamended and intentionally amended fertilisers this study developed empirical calibrations for a portable handheld X-ray fluorescence (pXRF) spectrometer, determined the reliability for estimating the macro and micro nutrients and evaluated the use of the pXRF for the high-throughput detection of trace element contaminants in fertilisers. The models developed using pXRF for Mg, P, S, K, Ca, Mn, Fe, Zn and Mo had R2 values greater or equal to 0.97. These models also performed well on validation, with R2 values greater or equal to 0.97 (except for Fe, R2val = 0.55) and slope values ranging from 0.81 to 1.44. A second set of models were developed with a focus on trace elements in amended fertilisers. The R2 values of calibration for Co, Ni, As, Se, Cd and Pb were greater than or equal to 0.80. At concentrations up to 1000 mg kg-1, good validation statistics were also obtained; R2 values ranged from 0.97–0.99, except in one instance. The regression coefficients of the validation also had good prediction in the range of 0–100 mg kg-1 (R2 values were from 0.78–0.99), but not as well at lower concentrations up to 20 mg kg-1 (R2 values ranged from 0.10–0.99), especially for Cd. This study has demonstrated that pXRF can measure several major (P, Ca) and micro (Mn, Fe, Cu) nutrients, as well as trace elements and potential contaminants (Cr, Ni, As) in fertilisers with high accuracy and precision. The results obtained in this study is good, especially considering that loose powders were scanned for a maximum of 90 seconds without the use of a vacuum pump

Evaluation of Genotype x Environment Interaction and Stability of Grain Yield and Related Yield Components in Pearl Millet (Pennisetum glaucum (L.) R.Br.)

Thirty six pearl millet genotypes were evaluated in randomized complete block design with two replications during 2011/2012 at two locations to study the magnitude of genotype by environment interaction for yield and yield related traits and identify the most stable high yielding genotypes. ANOVA of data at individual locations revealed significant differences among genotypes at Marigat and Koibatek for all yield components. Combined mean analysis of variance showed that the Genotype and location main effects and the genotype by environment interaction were highly significant (P≤0.01) for grain yield and other traits, indicating differential response of genotypes across testing locations and the need for stability analysis. Marigat was the most suitable environment and gave highest mean grain yield of 3620 kg/ha. The lowest yield 870 Kg/ha was recorded at Koibatek. Genotypes EUP 32, EUP 35, EUP 19 and EUP 10 produced high mean yield of 3530, 3080, 2690 and 2590 kg/ha respectively. The lowest grain 1290 kg/ha was obtained from genotype EUP 4.Based on the parameters of stability, three stable (widely adapted) and high yielding genotypes (EUP 34, EUP 18, and EUP 9) were identified. They also out-yield the standard open pollinated variety (OPV) check, Kat PM2. Genotypes EUP 32 was the highest yielding across all sites followed by EUP 35 and could be recommended for further multi-location evaluation in warmer environment and possible release for commercial production. The findings of this study showed that pearl millet hybrids have high potential for commercial production in Kenya than the OPVs. The ANOVA results showed that the effects of environments, genotypes and genotype x environment interaction (GE) were important in trait expression and performance of genotypes. In addition, it was observed that amount of rainfall received at both vegetative and post-anthesis phases and temperature had an effect on grain yield. The GGE biplot analysis characterised the environments in terms of stability and productivity, where Marigat was the best for grain yield; implying that environment-specific selection should be adopted. Genotypes EUP 34, EUP 18, and EUP 9 were the best performing since they out yielded the standard OPV check. These stable high yielding genotypes can be evaluated further in varied agro-ecologies and recommended for release as commercial hybrid varieties in ASALs of Kenya

Evaluation of Selected Pigeonpea (Cajanus cajan (L.) Millsp.) Genotypes for Resistance to Insect Pest Complex in Dry Areas of North Rift Valley, Kenya

Pigeonpea is an important pulse crop that has gained importance in semi-arid tropics, although its yield potential has not been fully realized due to biotic and abiotic stresses that limit its production. Insect pest complex of pod borer (Helicoverpa armigera), sucking bug (Clavigralla tomentosicollis) and pod fly (Melanagromyza cholcosoma) are the major limiting factors to its production causing up to 100% yield loss. The objective of this study was to evaluate resistant genotypes to insect pest complex in dry parts of North Rift Valley Kenya. The study was carried out in three sites (Kenya Agricultural Livestock Research Organization- Marigat, Agricultural Training Centre-Koibatek and Fluorspar-Chepsirei) for one season during long rain of April-November 2014 growing season. Sixteen ICRISAT elite genotypes were evaluated in randomized complete block design (RCBD) with 75cm inter and 25 cm intra spacing. Significant (P≤0.05) differences in grain yield performance, incidence and severity of the insect pests were revealed in all sites. The damage was more severe in Marigat (Pod borer-37.2%, Sucking bug-39.3% and pod fly-5.9%) than ATC- Koibatek (Pod borer-1.9%, Sucking bug-8.4% and pod fly-5.9%) and Fluorspar (Pod borer-3.6%, Sucking bug-6.8% and pod fly-2.9%). Genotypes ICEAPs 00850R, 00902, 01541 and 1154-2 showed potential levels of resistance to the insect pest complex and high yields. Grain yield associated negatively (P≤0.05) with pod borer and sucking bug damage correlated non-significantly with pod fly damage. The potential genotypes identified in this study need to be further evaluated in two seasons and in other multi-locations to validate these findings to be used in breeding

Performance of marker assisted backcross breeding (MABC) elite chickpea lines under drought conditions in Kenya

Drought is the most important constraint affecting production of chickpea and other crops as well. Quantitative traits like drought tolerance are multigenic and their inheritance is difficult to predict hence the need to explore more precise breeding techniques like maker assisted selection. The aim of this study was to introgress the identified root trait QTLs into Kenyan adapted cultivar to enhance drought tolerance through marker assisted backcrossing. Four varieties Chania Desi 1 (ICCV 97105), ICCV10, ICCV 92318, and Saina K1 (ICCV 95423) were selected as a recurrent parents for improvement among ten agronomically superior elite cultivars after exhibiting high polymorphism with SSR markers. Five molecular markers (CaM1903, CaM1502, TAA 170, NCPGR21 and GA11) were validated for use in MABC deployed in this study. Crosses were made between the four parents and ICC 4958 followed by marker screening of the F1 seedling progenies for the QTL of interest. Identified true heterozygotes were used as donors and backcrossed to the recurrent parent to obtain BC1F1 seeds. The process was repeated to obtain BC2F1 and finally BC3F1 with molecular marker identification of seedlings carrying the QTL region at each step. Results of evaluation in one trial site in Kenya semi-arid area (Koibatek ATC) of MABC lines for the four parents ICCV10 (24 lines), ICCV 92318 (8lines), ICCV 97105 (12 lines) and Saina K1-ICCV 95423 (10 lines) showed that the best progenies with higher levels of drought resistance and yield were ICCMABCD-21, 9, 20, 23, 15, 22, 5, 14, 16, 19 and 6 with yields > 2.5 tons/ha. The results indicated that it is possible to transfer QTL that confers drought tolerance using MABC. The best progenies are undergoing further evaluation to validate the contribution of the introgressed QTL in improving drought tolerance and yield

An overview of chickpea breeding programs in Kenya

Chickpea is a new crop in Kenya and its potential has not been fully utilized. The chickpea grain yields generally range between 1.2 to 3.5 tons/ha at farmers‟ fields, indicating that chickpea has a potential of becoming an important export crop in Kenya. The chickpea breeding program in Kenya is still at infant stage and being established with support from International Crops Research Institute for the Semi-Arid Tropics (ICRISAT). Four chickpea varieties have been recently released from the breeding material supplied by ICRISAT. Efforts are being made on evaluation of germplasm and breeding lines, application of modern molecular breeding tools and techniques in chickpea breeding and establishment of effective seed system for establishing a sustainable chickpea production system in the country

The nutritional quality of cereals varies geospatially in Ethiopia and Malawi

Micronutrient deficiencies (MNDs) remain widespread among people in sub-Saharan Africa1,2,3,4,5, where access to sufficient food from plant and animal sources that is rich in micronutrients (vitamins and minerals) is limited due to socioeconomic and geographical reasons4,5,6. Here we report the micronutrient composition (calcium, iron, selenium and zinc) of staple cereal grains for most of the cereal production areas in Ethiopia and Malawi. We show that there is geospatial variation in the composition of micronutrients that is nutritionally important at subnational scales. Soil and environmental covariates of grain micronutrient concentrations included soil pH, soil organic matter, temperature, rainfall and topography, which were specific to micronutrient and crop type. For rural households consuming locally sourced food—including many smallholder farming communities—the location of residence can be the largest influencing factor in determining the dietary intake of micronutrients from cereals. Positive relationships between the concentration of selenium in grain and biomarkers of selenium dietary status occur in both countries. Surveillance of MNDs on the basis of biomarkers of status and dietary intakes from national- and regional-scale food-composition data1,2,3,4,5,6,7 could be improved using subnational data on the composition of grain micronutrients. Beyond dietary diversification, interventions to alleviate MNDs, such as food fortification8,9 and biofortification to increase the micronutrient concentrations in crops10,11, should account for geographical effects that can be larger in magnitude than intervention outcomes

Interaction of Copper-Based Nanoparticles to Soil, Terrestrial, and Aquatic Systems: Critical Review of the State of the Science and Future Perspectives

In the past two decades, increased production and usage of metallic nanoparticles (NPs) has inevitably increased their discharge into the different compartments of the environment, which ultimately paved the way for their uptake and accumulation in various trophic levels of the food chain. Due to these issues, several questions have been raised on the usage of NPs in everyday life and has become a matter of public health concern. Among the metallic NPs, Cu-based NPs have gained popularity due to their cost-effectiveness and multifarious promising uses. Several studies in the past represented the phytotoxicity of Cu-based NPs on plants. However, comprehensive knowledge is still lacking. Additionally, the impact of Cu-based NPs on soil organisms such as agriculturally important microbes, fungi, mycorrhiza, nematode, and earthworms are poorly studied. This review article critically analyses the literature data to achieve a more comprehensive knowledge on the toxicological profile of Cu-based NPs and increase our understanding of the effects of Cu-based NPs on aquatic and terrestrial plants as well as on soil microbial communities. The underlying mechanism of biotransformation of Cu-based NPs and the process of their penetration into plants has also been discussed herein. Overall, this review could provide valuable information to design rules and regulations for the safe disposal of Cu-based NPs into a sustainable environment

Comprehensive nutrient analysis in agricultural organic amendments through non-destructive assays using machine learning

Portable X-ray fluorescence (pXRF) and Diffuse Reflectance Fourier Transformed Mid-Infrared (DRIFT-MIR) spectroscopy are rapid and cost-effective analytical tools for material characterization. Here, we provide an assessment of these methods for the analysis of total Carbon, Nitrogen and total elemental composition of multiple elements in organic amendments. We developed machine learning methods to rapidly quantify the concentrations of macro- and micronutrient elements present in the samples and propose a novel system for the quality assessment of organic amendments. Two types of machine learning methods, forest regression and extreme gradient boosting, were used with data from both pXRF and DRIFT-MIR spectroscopy. Cross-validation trials were run to evaluate generalizability of models produced on each instrument. Both methods demonstrated similar broad capabilities in estimating nutrients using machine learning, with pXRF being suitable for nutrients and contaminants. The results make portable spectrometry in combination with machine learning a scalable solution to provide comprehensive nutrient analysis for organic amendments