Posts Tagged ‘climate change’

How can we improve crop genotypes to increase stress resilience and productivity in a future climate? A new crop screening method based on productivity and resistance to abiotic stress

Posted by gabrielamartinez on , in Journal Articles

The need to accelerate the selection of crop genotypes that are both resistant to and productive under abiotic stress is enhanced by global warming and the increase in demand for food by a growing world population. In this paper, we propose a new method for evaluation of wheat genotypes in terms of their resilience to stress and their production capacity. The method quantifies the components of a new index related to yield under abiotic stress based on previously developed stress indices, namely the stress susceptibility index, the stress tolerance index, the mean production index, the geometric mean production index, and the tolerance index, which were created originally to evaluate drought adaptation. The method, based on a scoring scale, offers simple and easy visualization and identification of resilient, productive and/or contrasting genotypes according to grain yield. This new selection method could help breeders and researchers by defining clear and strong criteria to identify genotypes with high resilience and high productivity and provide a clear visualization of contrasts in terms of grain yield production under stress. It is also expected that this methodology will reduce the time required for first selection and the number of first-selected genotypes for further evaluation by breeders and provide a basis for appropriate comparisons of genotypes that would help reveal the biology behind high stress productivity of crops.

Source: Journal of Experimental Botany | Oxford Academic

Modelling and genetic dissection of staygreen under heat stress | SpringerLink

Posted by gabrielamartinez on , in Journal Articles

Plant chlorophyll retention—staygreen—is considered a valuable trait under heat stress. Five experiments with the Seri/Babax wheat mapping population were sown in Mexico under hot-irrigated environments. Normalized difference vegetation index (NDVI) during plant growth was measured regularly and modelled to capture the dynamics of plant greenness decay, including staygreen (Stg) at physiological maturity which was estimated by regression of NDVI during grainfilling. The rate of senescence, the percentage of plant greenness decay, and the area under the curve were also estimated based on NDVI measurements. While Stg and the best fitted curve were highly environment dependent, both traits showed strong (positive for Stg) correlations with yield, grainfilling rates, and extended grainfilling periods, while associations with kernel number and kernel weight were weak. Stg expression was largely dependent on rate of senescence which was related to the pattern of the greenness decay curve and the initial NDVI. QTL analyses revealed a total of 44 loci across environments linked to Stg and related traits, distributed across the genome, with the strongest and most repeatable effects detected on chromosomes 1B, 2A, 2B, 4A, 4B and 7D. Of these, some were common with regions controlling phenology but independent regions were also identified. The co-location of QTL for Stg and performance traits in this study confirms that the staygreen phenotype is a useful trait for productivity enhancement in hot-irrigated environments.

Source: Modelling and genetic dissection of staygreen under heat stress | SpringerLink

Resistance of Bt-maize (MON810) against the stem borers Busseola fusca (Fuller) and Chilo partellus (Swinhoe) and its yield performance in Kenya

Posted by gabrielamartinez on , in Journal Articles

A study was conducted to assess the performance of maize hybrids with Bt event MON810 (Bt-hybrids) against the maize stem borer Busseola fusca (Fuller) in a biosafety greenhouse (BGH) and against the spotted stem borer Chilo partellus (Swinhoe) under confined field trials (CFT) in Kenya for three seasons during 2013e2014. The study comprised 14 non-commercialized hybrids (seven pairs of near-isogenic Bt and non-Bt hybrids) and four non-Bt commercial hybrids. Each plant was artificially infested twice with 10 first instar larvae. In CFT, plants were infested with C. partellus 14 and 24 days after planting; in BGH, plants were infested with B. fusca 21 and 31 days after planting. In CFT, the seven Bt hybrids significantly differed from their non-Bt counterparts for leaf damage, number of exit holes, percent tunnel length, and grain yield. When averaged over three seasons, Bt-hybrids gave the highest grain yield (9.7 t ha1), followed by non-Bt hybrids (6.9 t ha1) and commercial checks (6 t ha1). Bt-hybrids had the least number of exit holes and percent tunnel length in all the seasons as compared to the non-Bt hybrids and commercial checks. In BGH trials, Bt-hybrids consistently suffered less leaf damage than their non-Bt near isolines. The study demonstrated that MON810 was effective in controlling B. fusca and
C. partellus. Bt-maize, therefore, has great potential to reduce the risk of maize grain losses in Africa due to stem borers, and will enable the smallholder farmers to produce high-quality grain with increased
yield, reduced insecticide inputs, and improved food security.

Source: Resistance of Bt-maize (MON810) against the stem borers Busseola fusca (Fuller) and Chilo partellus (Swinhoe) and its yield performance in Kenya

Predicting wheat maturity and stay–green parameters by modeling spectral reflectance measurements and their contribution to grain yield under rainfed conditions

Posted by gabrielamartinez on , in Journal Articles

The normalized difference vegetation index (NDVI) continues to provide easy and fast methodologies to characterize wheat genetic resources in response to abiotic stresses. This study identifies ways to maximize green leaf area duration during grain filling and develops NDVI models to predict physiological maturity and different stay −green parameters to increase grain yield of rainfed winter wheat under terminal drought. Three wheat populations were evaluated: one containing 240 landraces from Afghanistan, the second with 250 modern lines and varieties, tested for two years under low rainfall conditions in Turkey, and the third with 291 landraces from Central and Western Asia (grown for one year in the same location). The onset of senescence, maximum “greenness”, rate of senescence and residual “greenness” at physiological maturity were estimated using sequential measurements of NDVI and have shown significant correlations with grain yield under low rainfall rainfed conditions. Trade-offs were identified among the different stay −green attributes, e.g. delayed onset of senescence and high maximum “greenness” resulted in accelerated rates of senescence and highest yields and were most evident in the landrace populations. It is concluded, that the use of rate of senescence to select for stay −green must be coupled with other stay −green components, e.g. onset of senescence or maximum “greenness” to avoid the effects of the trade-offs on final grain yield. The NDVI decay curves (using the last three NDVI measurements up to maturity) were used to estimate days to maturity using the NDVI decay during the senescence period and days to heading. A training and testing set (20 and 80% of each population, respectively) were used for calibrations allowing for correlations between predicted and observed maturity of up to r = +0.85 (P < 0.0001). This procedure will facilitate large −scale wheat phenotyping in the future.

Source: Predicting wheat maturity and stay–green parameters by modeling spectral reflectance measurements and their contribution to grain yield under rainfed conditions

Genomic prediction models for grain yield of spring bread wheat in diverse agro-ecological zones : Scientific Reports

Posted by gabrielamartinez on , in Journal Articles

Genomic and pedigree predictions for grain yield and agronomic traits were carried out using high density molecular data on a set of 803 spring wheat lines that were evaluated in 5 sites characterized by several environmental co-variables. Seven statistical models were tested using two random cross-validations schemes. Two other prediction problems were studied, namely predicting the lines’ performance at one site with another (pairwise-site) and at untested sites (leave-one-site-out). Grain yield ranged from 3.7 to 9.0 t ha−1across sites. The best predictability was observed when genotypic and pedigree data were included in the models and their interaction with sites and the environmental co-variables. The leave-one-site-out increased average prediction accuracy over pairwise-site for all the traits, specifically from 0.27 to 0.36 for grain yield. Days to anthesis, maturity, and plant height predictions had high heritability and gave the highest accuracy for prediction models. Genomic and pedigree models coupled with environmental co-variables gave high prediction accuracy due to high genetic correlation between sites. This study provides an example of model prediction considering climate data along-with genomic and pedigree information. Such comprehensive models can be used to achieve rapid enhancement of wheat yield enhancement in current and future climate change scenario.

Source: Genomic prediction models for grain yield of spring bread wheat in diverse agro-ecological zones : Scientific Reports

High temperatures around flowering in maize : effects on photosynthesis and grain yield in three genotypes

Posted by gabrielamartinez on , in Journal Articles

57958Authors: Neiff, N.; Trachsel, S.; Valentinuz, O.R.; Balbi, C.N.; Andrade, H.F.

Published in : Crop Science Society of America Crop Science 2016, vol.56, p.1-11

To aid breeding for heat-tolerant germplasm we analyzed the effects of high temperatures on the CO2 exchange rate (CER), crop growth rate (CGR), kernel number (KN), and grain yield (GY) in a 30-d period bracketing flowering. Field experiments, including three maize (Zea mays L.) hybrids with temperate (Te), tropical (Tr) and temperate × tropical (Tx) adaptation were performed in two experiments (Exp. 1 and 2). Hybrids were subjected to high temperatures induced by shelters during a 15-d period before (H1; preflowering) or after silking (H2; postflowering). Crop growth rate was measured during the 30-d period bracketing silking (CGRCP), H1 (CGRPRE), and H2 (CGRPOST). Relative to nonstressed conditions, CER was reduced by 17 and 16% in H1 and H2. Moreover, CER was associated with CGRCP (r = 0.78; p ≤ 0.001), CGRPRE (r = 0.39; p ≤ 0.05), CGRPOST (r = 0.51; p ≤ 0.01), KN (Exp. 1, r = 0.53; p ≤ 0.01; Exp. 2, r = 0.49; p ≤ 0.01), and GY (Exp. 1, r = 0.59; p ≤ 0.01; Exp. 2, r = 0.46; p ≤ 0.05). As a result of heat stress, CGRCP (H1, −17%; H2, −29%), KN (H1, −7%; H2, −45%), and GY (H1, −10%; H2, −45%) were reduced relative to the control treatment. Stronger reductions for all traits in H2 relative to H1 emphasize the importance of sufficient CER during this period. The effect of high temperature on CER differed among hybrids (Tx > Te = Tr) and is promising for future germplasm screening.

Targeting drought-tolerant maize varieties in Southern Africa : a geospatial crop modeling approach using big data

Posted by gabrielamartinez on , in Journal Articles

57959Authors: Kindie Tesfaye Fantaye.; Sonder, K.; Cairns, J.E.; Magorokosho, C.; Amsal Tesfaye Tarekegne; Kassie, G.; Getaneh, F.; Abdoulaye, T.; Tsedeke Abate; Erenstein, O.

In: The International Food and Agribusiness Management Review 2016, vol. 19 (Special Issue A), p.75-92

Maize is a major staple food crop in southern Africa and stress tolerant improved varieties have the potential to increase productivity, enhance livelihoods and reduce food insecurity.

This study uses big data in refining the geospatial targeting of new drought-tolerant (DT) maize varieties in Malawi, Mozambique, Zambia, and Zimbabwe. Results indicate that more than 1.0 million hectares (Mha) of maize in the study countries is exposed to a seasonal drought frequency exceeding 20% while an additional 1.6 Mha experience a drought occurrence of 10–20%. Spatial modeling indicates that new DT varieties could give a yield advantage of 5–40% over the commercial check variety across drought environments while crop management and input costs are kept equal. Results indicate a huge potential for DT maize seed production and marketing in the study countries. The study demonstrates how big data and analytical tools enhance the targeting and uptake of new agricultural technologies for boosting rural livelihoods, agribusiness development and food security in developing countries.

Climate variability and yield risk in South Asia’s rice–wheat systems : emerging evidence from Pakistan

Posted by gabrielamartinez on , in Journal Articles

57981Authors: Muhammad Arshad.; Amjath-Babu, T.S.; Krupnik, T.J.; Aravindakshan, S.; Abbas, A.; Kachele, H.; Muller, K.

Published in: Paddy Water Environment, In press.

Rice and wheat are the principal calorie sources for over a billion people in South Asia, although each crop is particularly sensitive to the climatic and agronomic management conditions under which they are grown. Season-long heat stress can reduce photosynthesis and accelerate senescence; if extreme heat stress is experienced during flowering, both rice and wheat may also experience decreased pollen viability and stigma deposition, leading to increased grain sterility. Where farmers are unable to implement within-season management adaptations, significant deviations from expected climatic conditions would affect crop growth, yield, and therefore have important implications for food security. The influence of climatic conditions on crop growth have been widely studied in growth chamber, greenhouse, and research station trials, although empirical evidence of the link between climatic variability and yield risk in farmers’ fields is comparatively scarce. Using data from 240 farm households, this paper responds to this gap and isolates the effects of agronomic management from climatic variability on rice and wheat yield risks in eight of Pakistan’s twelve agroecological zones. Using Just and Pope production functions, we tested for the effects of crop management practices and climatic conditions on yield and yield variability for each crop. Our results highlight important risks to farmers’ ability to obtain reliable yield levels for both crops. Despite variability in input use and crop management, we found evidence for the negative effect of both season-long and terminal heat stress, measured as the cumulative number of days during which crop growth occurred above critical thresholds, though wheat was considerably more sensitive than rice. Comparing variation in observed climatic parameters in the year of study to medium-term patterns, rice, and wheat yields were both negatively affected, indicative of production risk and of farmers’ limited capacity for within-season adaptation. Our findings suggest the importance of reviewing existing climate change adaptation policies that aim to increase cereal farmers’ resilience in Pakistan, and more broadly in South Asia. Potential agronomic and extension strategies are proposed for further investigation.

Baseline simulation for global wheat production with CIMMYT mega-environment specific cultivars

Posted by gabrielamartinez on , in Journal Articles

57984Authors: Gbegbelegbe, S.D.; Cammarano, D.; Asseng, S.; Robertson, R.; Chung, U.; Adam, M.; Abdalla, O.; Payne, T.S.; Reynolds, M.P.; Sonder, K.; Shiferaw, B.; Nelson, G.

Published in: Field Crops Research, In press.

Climate change is expected to impact global food supply and food security by affecting growing conditions for agricultural production. Process-based dynamic growth models are important tools to estimate crop yields based on minimum inputs of climate, soil, crop management, and crop cultivar parameters. Using region-specific cultivar parameters is critical when applying crop models at a global scale because cultivars vary in response to climate conditions, soils, and crop management. In this study, parameters were developed for modern cultivars representing all 17 CIMMYT wheat Mega Environments (MEs) using field experimental data and genetic cultivar relationships for the CROPSIM-CERES model in DSSAT v 4.5 (Decision-Support System for Agrotechnology Transfer). Cultivar performance was tested with independent CIMMYT breeding trial field experiments across several locations. Then crop simulations were carried out at 0.5 × 0.5 ° pixels for global wheat-growing areas, using cultivars representing MEs, soil information, region-specific crop management, and initial soil conditions. Aggregated simulated wheat yields and production were compared to reported country yields and production from Food and Agriculture Organization (FAO) statistics, resulting in a Root Mean Square Error (RMSE) of 1.3 t/ha for yield and 2.2 M t/country for country production. Some of the simulated errors are relatively large at country level because of uncertainties in pixel information for climate, soil, and crop management input and partly because of crop model uncertainties. In addition, FAO yield statistics have uncertainties because of incomplete farm reports or poor estimates. Nevertheless, this new cultivar-specific, partially-validated global baseline simulation enables new studies on issues of food security, agricultural technology, and breeding advancement impacts combined with climate change at a global scale.

Developing local adaptation strategies for climate change in agriculture: a priority-setting approach with application to Latin America

Posted by Carelia Juarez on , in Journal Articles

Published in Global Environmental Change 29:78-91, 2014.

Lee, D.R.; Edmeades, S.; De Nys, E.; McDonald, A.; Janssen, W.

Even with substantially increased attention to climate adaptation in developing countries in recent years, there are a number of important remaining research needs: better incorporating stakeholder input; using replicable methodologies to provide comparability across different settings; assuring that stakeholder input reflects the results of climate science, not simply perceptions; and effectively linking stakeholder input with the regional and national levels at which policy changes are made. This study reports the results of a methodology for identifying and prioritizing local, stakeholder-driven response options to climate change in agriculture. The approach is based on multi-criteria scoring methods previously applied to research planning and priority-setting in agricultural and natural resource management research, public health, and other areas. The methodology is a sequential approach built around needs assessments by local stakeholders; the incorporation of climate science results; the sharing of these results and climate adaption response options with stakeholders at a series of workshops; stakeholder priority-setting exercises using multi-criteria scoring; and validation with policymakers. The application is to three diverse agroecosystems in Mexico, Peru and Uruguay. Among the many findings is that, notwithstanding the wide diversity of agro-ecosystems, there are numerous similarities in the agricultural adaptation responses prioritized by local stakeholders.