Towards shifting planting date as an adaptation practice for rainfed wheat response to climate change

Nouri, Milad; Homaee, Mehdi; Bannayan, Mohammad; Hoogenboom, Gerrit

doi:10.1016/j.agwat.2017.03.004

Cited by 81 publications

(37 citation statements)

References 49 publications

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“…Moreover, an increase in temperature also results in increases in respiration losses, including dark respiration [53,54] and photorespiration [55], therefore affecting yield negatively. Previous studies have concluded that the response of the crop to shifting the planting date is a region-specific adaptation strongly influenced by climate conditions [56][57][58]. The loss of yield reported in this study agrees with the model-predicted winter wheat yields in semi-arid regions [59].…”

Section: Implications Of Growing Conditions On Final Gysupporting

confidence: 87%

UAV and Ground Image-Based Phenotyping: A Proof of Concept with Durum Wheat

et al. 2019

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Climate change is one of the primary culprits behind the restraint in the increase of cereal crop yields. In order to address its effects, effort has been focused on understanding the interaction between genotypic performance and the environment. Recent advances in unmanned aerial vehicles (UAV) have enabled the assembly of imaging sensors into precision aerial phenotyping platforms, so that a large number of plots can be screened effectively and rapidly. However, ground evaluations may still be an alternative in terms of cost and resolution. We compared the performance of red-green-blue (RGB), multispectral, and thermal data of individual plots captured from the ground and taken from a UAV, to assess genotypic differences in yield. Our results showed that crop vigor, together with the quantity and duration of green biomass that contributed to grain filling, were critical phenotypic traits for the selection of germplasm that is better adapted to present and future Mediterranean conditions. In this sense, the use of RGB images is presented as a powerful and low-cost approach for assessing crop performance. For example, broad sense heritability for some RGB indices was clearly higher than that of grain yield in the support irrigation (four times), rainfed (by 50%), and late planting (10%). Moreover, there wasn't any significant effect from platform proximity (distance between the sensor and crop canopy) on the vegetation indexes, and both ground and aerial measurements performed similarly in assessing yield.2 of 25 principal goal for breeders. Durum wheat is, by extension, the main cereal cultivated on the southern and eastern shores of the Mediterranean Basin and one of the main cereals in southern Europe [4].Yield is a phenotypically complicated trait, not only because of its genetic complexity [5], but also due to the relative magnitude of gene-environment interactions [6,7], and it is one of the most integrative traits influenced by known and unknown factors. Thus, genotype evaluations in multi-environment trials are needed, at least in the advanced (generations) stages of selection. However, the major point at issue is that high-throughput plant phenotyping (HTPP) may still represent a bottleneck in breeding programs [7], owing to the need to increase the accuracy, precision, and throughput of the methodologies used, while reducing costs and minimizing labor [8,9]. Furthermore, HTPP approaches should allow multi-temporal trait-specific measurements to evaluate the yield components at different phenological moments.Nowadays, and almost by definition, HTPP implies the use of non-invasive remote sensing approaches of different nature [5,10], given the possibility of screening larger populations faster than conventional phenotyping procedures. Moreover, recent progress and advances in the technology of aeronautics and sensors have allowed the adoption of unmanned aerial vehicle (UAV) platforms, capable of precisely screening hundreds of plots in a short period of time [7,11]. Further benefits of the simultaneous c...

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Section: Implications Of Growing Conditions On Final Gysupporting

confidence: 87%

UAV and Ground Image-Based Phenotyping: A Proof of Concept with Durum Wheat

et al. 2019

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“…This indicates that performance was acceptable for the three traits, but less satisfactory for LAI. Model performance is considered excellent at nRMSE values of less than 10%, good for nRMSE values of 10 to 20%, fair for nRMSE values of 20 to 30% and poor for nRMSE values greater than 30% (Nouri et al, 2017). These results are similar to those of…”

Section: Model Performancesupporting

confidence: 62%

Analysis of growth functions that can increase irrigated wheat yield under climate change

Eyni‐Nargeseh

Deihimfard

Rahimi‐Moghaddam

et al. 2019

Meteorological Applications

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This study assessed the growth of wheat under climate change conditions (increases in CO2 and temperature) in southwestern Iran. Future climate data were projected using long‐term climate data for radiation and minimum and maximum temperatures for the baseline period 1980–2010. The future climate scenarios made use of delta changes from the Coupled Model Intercomparison Project Phase 5 (CMIP5) in the R package of the Agricultural Model Intercomparison and Improvement Project for 2040–2070. After generating the weather data, the Agricultural Production System Simulator wheat model was applied to assess the potential impact of climate change on wheat yield and the relative growth rate, crop growth rate, total dry weight and leaf area index. Averaged across all locations, an increase of 1.6 and 2.3°C in annual temperature was projected under representative concentration pathway 4.5 (RCP4.5) and RCP8.5, respectively. The decrease in the average length of the growing season was 7.5 and 9.3% under RCP4.5 and RCP8.5, respectively, in the mid‐21st century with respect to baseline. The mean cumulative radiation will decrease by 11.6 and 14.3% averaged across all sites for RCP4.5 and RCP8.5, respectively. Grain yield will decrease at all sites for both RCPs. The greatest decrease in wheat yield was projected for Izeh under RCP4.5 and RCP8.5 (7.9 and 9.3%, respectively) for 2040–2070 relative to baseline. A greater grain yield at baseline than for the RCP8.5 and RCP4.5 scenarios was closely related to higher leaf area index, relative growth rate and crop growth rate values during the reproductive phase. Thus, these parameters can be defined as indicators of crop yield.

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“…Changing planting date is considered an effective adaptive strategy to mitigate the negative impacts of climate change on crop yields on a global scale, such as in Northeast China for maize 10 , in Burkina Faso for maize 11 , in the west and northwest Iran for wheat 12 and in Canada for spring wheat and maize 14 . These adverse impacts of climate change could be partially offset by optimal planting date, mainly due to the ability to match crop growth with changed temperature and rainfall distribution.…”

Section: Discussionmentioning

confidence: 99%

“…Many potential adaptation strategies have been explored to mitigate the negative impacts of maize yield under climate change conditions. Several studies indicated that adjustment of crop phenology could be beneficial for mitigating yield loss under future climate scenarios (e.g., elongating maturity and changing sowing date) [10][11][12] . For examples, Lin et al 10 showed that maize yield loss could be mitigated by substituting local cultivars with latermaturing and delaying the planting date in Heilongjiang province, Northeast China under future climate change.…”

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confidence: 99%

“…The DSSAT model is a widely used tool for testing cropping technologies, assessing management practices, and exploring climate change mitigation stategies 14,[21][22][23][24] . The DSSAT model has been successfully used to optimize field management practices to achieve high crop yield, improve understandings of crop physiology, soil management and weather effects on crop growth and environmental quality 25,26 , and explore the responses of crop production to climate change and develop effective adaptation strategies 10,12,14,27 . Although the DSSAT model has been used to simulate the impacts of climate change on maize yield, no detailed adaptation strategies were assessed in simulations by considering comprehensive management practices for improving maize production and N use efficiency in Northeast China.…”

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confidence: 99%

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Modelling adaptation strategies to reduce adverse impacts of climate change on maize cropping system in Northeast China

Jiang

et al. 2021

Sci Rep

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Maize (Zea mays L.) production in Northeast China is vulnerable to climate change. Thus, exploring future adaptation measures for maize is crucial to developing sustainable agriculture to ensure food security. The current study was undertaken to evaluate the impacts of climate change on maize yield and partial factor productivity of nitrogen (PFPN) and explore potential adaptation strategies in Northeast China. The Decision Support System for Agrotechnology Transfer (DSSAT) model was calibrated and validated using the measurements from nine maize experiments. DSSAT performed well in simulating maize yield, biomass and N uptake for both calibration and validation periods (normalized root mean square error (nRMSE) < 10%, −5% < normalized average relative error (nARE) < 5% and index of agreement (d) > 0.8). Compared to the baseline (1980–2010), the average maize yields and PFPN would decrease by 7.6–32.1% and 3.6–14.0 kg N kg−1 respectively under future climate scenarios (2041–2070 and 2071–2100) without adaptation. Optimizing N application rate and timing, establishing rotation system with legumes, adjusting planting dates and breeding long-season cultivars could be effective adaptation strategies to climate change. This study demonstrated that optimizing agronomic crop management practices would assist to make policy development on mitigating the negative impacts of future climate change on maize production.

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Towards shifting planting date as an adaptation practice for rainfed wheat response to climate change

Cited by 81 publications

References 49 publications

UAV and Ground Image-Based Phenotyping: A Proof of Concept with Durum Wheat

UAV and Ground Image-Based Phenotyping: A Proof of Concept with Durum Wheat

Analysis of growth functions that can increase irrigated wheat yield under climate change

Modelling adaptation strategies to reduce adverse impacts of climate change on maize cropping system in Northeast China

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