Approach and methodology for climate risk assessments.
For select crops including: canola, corn, oats, soybean, sugar beet, sugarcane, sunflower, wheat, and table grapes
For select crops including: canola, corn, oats, soybean, sugar beet, sugarcane, sunflower, wheat, and table grapes
Prepared by the Alliance of Bioversity-CIAT
Objective of the climate risk assessment
Climate change can intensify already existing challenges for farmers and suppliers across the world. Impacts of climate change, in the form of higher temperatures and highly varied precipitation will significantly affect crop performance. There is an urgent need from decision makers and farmers for detailed information on magnitude of climate change impacts, strategy for climate change adaptation, and implication on business operation.
The Alliance of Bioversity-CIAT carried out the climate risk study with the aim to increase understanding within the supply chain of local risks and opportunities arising from climate change and creating a resilient farming system.
The goal of this methodology document is to make the study’s approach transparent and accessible by users of the Climate Resilience Platform. Section I covers background information on the climate data used in the analyses and the advantages of this data in a science-based approach. Section II describes the wider-reaching approach to anticipate climate impact on commodities more generally.
I. Approach to understand the influence of climate change and variability on crop productivity
Overall approach
An improved understanding of the resilience of global crop production and how this may shift with climate change is urgently needed. Two main categories of factors that have great influence on plant growth as well as on the increase of crop yield/production are biotic and abiotic factors. Biotic factors (e.g., crop variety) and abiotic factors (e.g., temperature, rain, humidity, solar radiation, soil moisture etc.) affect plant growth and crop yield. Agricultural crops normally undergo a series of physiological processes during phenological stages of their life cycles that are sensitive to different environmental and climate conditions.
Several studies indicate strong associations between climate change and agricultural crops, either by using empirical (statistical) models, mechanistic (process-based) approaches or a combination of both. However, there are only a few studies that focus on this relationship at the plot level over large domains or countries due to lack of data, underscoring the importance and value of these data. Empirical models can be used to predict how these factors drive crop yields (Schlenker and Roberts 2009; Lobell and Burke 2010; Lobell et al. 2011; Urban et al. 2012; Osborne and Wheeler 2013; Moore and Lobell 2014; Ray et al. 2015). These models use local environmental conditions, including climate data as well as all available grower data. Previous research has shown that these empirical models are well-suited to determine the impact of climate and agricultural practices on growth and development, and that they can be a useful tool to assess the long-term impact of climate and associated environmental risks on crop yield.
As no detailed plot-level grower data is available to inform an agri-climatic modelling approach, the study leverages public data from scientific literature and expert interviews to give a global view on more than 8 crops in over 50 countries. Harnessing the knowledge in the vast amount of scientific literature published on these crops globally, this proves to be a robust option in the absence of field data direct from growers’ plots. As peer review literature for each crop and growing regions is gathered, relevant information on growing calendars, climate-crop sensitivities, best climate models, and adaptation practices is extracted and organized. This helps associate crop performance with specific types of climate risk and provides a case for adaptation priorities. Detailed steps for the climate risk assessment approach are described in Section II of this document.
Climate data
Climate data can be separated into baseline (current) and future.
Baseline (current) climate data: Gridded climate data are acquired from the fifth generation of the European ReAnalysis, hereafter ERA5-Land (Muñoz-Sabater et al. 2021). Gridded climate data gives us access to daily high-resolution climatological data based on direct observations and over long time periods. The reason this is used is that there are not enough weather stations worldwide to cover every point on the earth. Gridded climate data solves this issue. These data describe the evolution of the water and energy cycles over land globally, at a 9km resolution. This is achieved through global high-resolution numerical modelling of the European Centre for Medium-Range Weather Forecasts (ECMWF) land surface model, which is driven by the downscaled meteorological forcing from the ERA5-Land climate reanalysis. Due to the scarcity of equally distributed on-the-ground weather stations, as well as the limitations point data incurs, ERA5-Land provides the best representation of high-resolution and reliable climate data source covering the globe. There are alternative gridded climate products providing similar data such as Worldclim and TerraClimate (Fick and Hijmans 2017; Abatzoglou et al. 2018), however these are only at monthly resolution.
Future climate data: Future daily climate are extracted from CMIP6 (Eyring et al. 2016). CMIP6 data (Coupled Model Intercomparison Project Phase 6 data) refers to a comprehensive set of climate model simulations that are designed to simulate the Earth's climate system and predict future climate change.
CMIP6 consists of 134 models from 53 modelling centers (Durack [2016] 2020). The scientific analyses from CMIP6 are used extensively in the Intergovernmental Panel on Climate Change 6th Assessment Report and are the most trusted source on future climate projections. These scenarios are highly flexible and allow for assessment of climate change impacts on crop production in an interpretable way while accounting for the uncertainty that is implicitly part of climate model projections and emission scenarios. The Climate Model Intercomparison Project (CMIP) was established in 1995 by the World Climate Research Program to provide climate scientists with a database of coupled Global Circulation Model (GCM) simulations. CMIP6 is the sixth and latest iteration of the leading international effort to bring together climate modelers from around the world to improve our understanding of past, present, and future climate change.
II. Climate risk assessment
Process overview
To achieve this, a three-step approach is applied. In the first step, existing literature is compiled to understand the main growing seasons of each crop in each focal country and the extent of positive and negative impacts of climate factors on each specific crop yield. Then, the best climate models are identified to represent different countries' baseline and future (2030) climate. Finally, the compiled data and selected climate models are used to project the impact and adaptation practices on crop yields in each of the crops main growing season, which is visualized on an interactive dashboard. This approach enables stakeholders to better understand the impacts of climate extremes on crop yields and identify effective adaptation strategies to ensure food security and sustainable agriculture in the future.
1. Understand climate impact
Daily climate data are extracted for each crop per country. These data cover both the baseline (current climate) as well as future. The baseline period is considered the period from 1980-2000. The reason an average of 20 years is taken is to ensure that no biases are included by selecting a particular year which may be unrepresentatively hot, dry, cold or wet. The future climate period is also based on a 20 year span covering the years 2020-2040, which is typically used for a 2030 climate. Certain GCM’s are more powerful than others depending on the geographic region. Therefore, we selected a different subset of GCM’s for each region depending on the performance for that country or region. The selection is expanded on below in “Quantify climate impact”. This data is used to produce four indices representing climate extremes that result in the largest net yield gain or loss risk for each jurisdiction during the growing season: heat, frost, flood, and drought. These risks are defined and calculated as follows:
In addition, the growing season, or the period between planting dates and harvesting dates, varied for each crop and country. This information is gathered from peer-review papers and databases from official sources. The list of reference sources for growing season is detailed in Annex B.
2. Quantify climate impact
A systematic review methodology is applied to assess all existing literature on crop limits and limiting factors to quantify the greatest limiting factors and ranges on crop yield. To achieve this, a comprehensive search of scientific databases is carried out, including peer-reviewed articles, conference proceedings, and scientific reports. The retrieved articles are then screened for relevance and quality and selected those that met our inclusion criteria. Specific data on crop types, yield, and limiting factors are extracted from the selected articles and synthesized using meta-analysis techniques. This approach enables the quantitative estimation of the relative importance of different climate risks (heat, frost, flood, drought) and their ranges on crop yield across different crop types, utilizing the vast amount of published literature on these crops. All crops and countries are shown below in Table 1. The climate risks’ ranges on different crops are specified in Annex C and the list of literature on limiting factors is detailed in Annex D.
The accuracy of GCMs in predicting climate conditions in different countries depends on a complex interplay of data quality, model assumptions and emission scenarios. Using all available academic literature, GCMs are gathered according to their accuracy per country, per region and all selected GCMs are averaged to produce an ensemble frost, drought, heat, and flood risk index per country. This ensures that only the best models are selected to better capture the complexity of the Earth's climate system and improves the ability to predict future climate change. The list of literature for GCMs assessment is detailed in Annex E.
3. Project impact of climate adaptation practices
Based on the criteria established in the "Quantify climate impact" section above, the crop yield of each crop is calculated for specific administrative levels, using categories of extremely high yield loss, high yield loss, moderate yield loss, no change, moderate yield gain, high yield gain, and extremely high yield gain. Each of these categories is color coded and visualized on country as well as global maps. Because crops are affected by all four climate risks while each administrative level on a map can only show one risk, the more significant risk will be prioritized in displaying on the map. This means that high yield gain/loss trumps medium gain/loss trumps low gain/loss irrespective of positive or negative impact. The cases where level of negative risk equals to that of positive risk, negative risk will be displayed. This process is conducted for both the projected climate conditions and a hypothetical simulation of a scenario that visualizes the potential adaptation impact of regenerative agriculture practices, referred to as a resilient scenario.
The resilient scenario is created based on a systematic literature review. For each location, a search is conducted to identify a range of options that can reduce the risk and enhance farm resilience. These options include changes in agricultural practices such as drip irrigation, cover cropping, or pest & disease scouting, etc. With the literature review, current adoption rates are assessed to determine the practices’ feasibility and opportunity to increase climate resilience. The process also involves assessing the potential benefits and limitations of each option with regards to their impact on livelihoods, greenhouse gas reduction and biodiversity. Based on previous assessments, a list of adaptation options is generated and prioritized with multiple factors across their feasibility, sustainability, and constraints to develop implementation plan. After identifying adaptation measures for each specific region, adaptation options are integrated into the base map with their yield benefits. Adaptation scenarios are then overlayed to see how effectively packages of adaptation options would reduce climate risks. The list of literature used for the resilient pathway development is detailed in Annex F.
Subsequently, these maps are also overlayed by production area (Monfreda, Ramankutty, and Foley 2008) to visualize the magnitude of the impact as well as the simulation of a potential resilient scenario. The results are integrated into an interactive dashboard – Climate Resilience Platform. Within the dashboard each admin level is mapped according to its greatest limiting factor.
III. Climate Resilience Platform
Results and outcomes from the project are integrated into a dynamic dashboard named “Climate Resilience Platform”. The dashboard also provides other complementary data layers for users to have a full picture of historical and projected climate, climate extremes, water risks, and topography. These data layers are acquired from various reputable sources and are widely referred to in the scientific community.
The historical climate data are acquired from ERA5-Land (Muñoz-Sabater et al. 2021) while the projected climate data are extracted from CMIP6 (Eyring et al. 2016) as mentioned earlier. The climate extreme indices include water risk layers acquired from the World Resources Institute. These water risk indices are compiled from advances in hydrological modelling, remotely sensed data, and published datasets (Kuzma et al. 2023).The frost index is acquired from the Global Agro-Ecological Zone (GAEZ) Data Portal. GAEZ is the result from collaboration between FAO (The Food and Agriculture Organization of the United Nations) and IISA (International Institute for Applied Systems Analysis) and it comprises a large volume of spatial natural resources indicators (“FAO and IIASA. Global Agro Ecological Zones Version 4 (GAEZ V4)” 2023). In addition, a topography layer is acquired from mundialis GmbH & Co.KG’s Web Map Services (“Web Map Services (WMS) — Mundialis — Free Data with Free Software” 2023). The list of data sources and definitions for these data layers is detailed in Annex H.
References:
Annex A – Climate data
Durack, PJ. (2016) 2020. “CMIP6_CVs.” Python. WCRP-CMIP.
https://github.com/WCRP-CMIP/CMIP6_CVs
Muñoz-Sabater, Joaquín, Emanuel Dutra, Anna Agustí-Panareda, Clément Albergel, Gabriele Arduini, Gianpaolo Balsamo, Souhail Boussetta, et al. 2021. “ERA5-Land: A State-of-the-Art Global Reanalysis Dataset for Land Applications.” Earth System Science Data 13 (9): 4349–83. https://doi.org/10.5194/essd-13-4349-2021
Eyring, Veronika, Sandrine Bony, Gerald A. Meehl, Catherine A. Senior, Bjorn Stevens, Ronald J. Stouffer, and Karl E. Taylor. 2016. “Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) Experimental Design and Organization.” Geoscientific Model Development 9 (5): 1937–58. https://doi.org/10.5194/gmd-9-1937-2016
Fick, Stephen E., and Robert J. Hijmans. 2017. “WorldClim 2: New 1-Km Spatial Resolution Climate Surfaces for Global Land Areas.” International Journal of Climatology 37 (12): 4302– 15. https://doi.org/10.1002/joc.5086
Abatzoglou, John T., Solomon Z. Dobrowski, Sean A. Parks, and Katherine C. Hegewisch. 2018. “TerraClimate, a High-Resolution Global Dataset of Monthly Climate and Climatic Water Balance from 1958–2015.” Scientific Data 5 (1): 170191.
https://doi.org/10.1038/sdata.2017.191
Annex B – Crop calendars and data sources
Annex C – Climate risks’ ranges
Climate risk definitions
Baseline
Annex D – Climate impacts on crops’ performance
Oats
Chinnici, M. F., & Peterson, D. M. (1979). Temperature and Drought Effects on Blast and Other Characteristics in Developing Oats 1. Crop Science, 19(6), 893-897.
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Gusta, L. V., & O’connor, B. J. (1987). Frost tolerance of wheat, oats, barley, canola and mustard and the role of ice-nucleating bacteria. Canadian Journal of Plant Science, 67(4), 1155-1165.
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Mushtaq, A., & Mehfuza, H. (2014). A review on oat (Avena sativa L.) as a dual-purpose crop. Scientific Research and Essays, 9(4), 52-59.
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Xie, H., Li, M., Chen, Y., Zhou, Q., Liu, W., Liang, G., & Jia, Z. (2021). Important physiological changes due to drought stress on oat. Frontiers in Ecology and Evolution, 271.oats
Xu, C. (2012). A study on growth characteristics of different cultivars of oat (Avena sativa) in alpine region. Acta Prataculturae Sinica, 21(2), 280-285.
Corn
Awika, J. M. (2011). Major cereal grains production and use around the world. In Advances in cereal science: implications to food processing and health promotion (pp. 1-13). American Chemical Society.
Butler, E. E., Mueller, N. D., & Huybers, P. (2018). Peculiarly pleasant weather for US maize. Proceedings of the National Academy of Sciences, 115(47), 11935-11940.
Hallauer, A. R., & Carena, M. J. (2009). Maize. In Cereals (pp. 3-98). Springer, New York, NY.
Lobell, D. B., & Asner, G. P. (2003). Climate and management contributions to recent trends in US agricultural yields. Science, 299(5609), 1032-1032.
Ramadoss, M., Birch, C. J., Carberry, P. S., & Robertson, M. (2004). Water and high temperature stress effects on maize production. In Proceedings of the 4th International Crop Science Congress, Brisbane, Australia (Vol. 26, pp. 45-49).
Ramadoss, M., Birch, C. J., Carberry, P. S., & Robertson, M. (2004). Water and high temperature stress effects on maize production. In Proceedings of the 4th International Crop Science Congress, Brisbane, Australia (Vol. 26, pp. 45-49).
Schlenker, W., & Roberts, M. J. (2009). Nonlinear temperature effects indicate severe damages to US crop yields under climate change. Proceedings of the National Academy of sciences, 106(37), 15594-15598.
Wilson, D. R., Johnstone, J. V., & Salinger, M. J. (1994). Maize production potential and climatic risk in the South Island of New Zealand. New Zealand Journal of Crop and Horticultural Science, 22(3), 321-334.
Sugarbeet
Clarke, N. A. H. H., Hetschkun, H., Jones, C., Boswell, E., & Marfaing, H. (1993). Identification of stress tolerance traits in sugar beet. In Interacting stresses on plants in a changing climate (pp. 511-524). Springer, Berlin, Heidelberg.
Hoffmann, C. M. (2010). Sucrose accumulation in sugar beet under drought stress. Journal of agronomy and crop science, 196(4), 243-252.
Hoffmann, C. M., & Kluge-Severin, S. (2011). Growth analysis of autumn and spring sown sugar beet. European journal of agronomy, 34(1), 1-9.
Jaggard, K. W., Dewar, A. M., & Pidgeon, J. D. (1998). The relative effects of drought stress and virus yellows on the yield of sugarbeet in the UK, 1980–95. The Journal of Agricultural Science, 130(3), 337-343.
Moliterni, V. M. C., Paris, R., Onofri, C., Orrù, L., Cattivelli, L., Pacifico, D., ... & Mandolino, G. (2015). Early transcriptional changes in Beta vulgaris in response to low temperature. Planta, 242(1), 187-201.
Petkeviciene, B. (2009). The effects of climate factors on sugar beet early sowing timing. Agron. Res, 7, 436-443.
Pidgeon, J. D., Ober, E. S., Qi, A., Clark, C. J., Royal, A., & Jaggard, K. W. (2006). Using multi-environment sugar beet variety trials to screen for drought tolerance. Field crops research, 95(2-3), 268-279.
Reinsdorf, E., Koch, H. J., & Märländer, B. (2013). Phenotype related differences in frost tolerance of winter sugar beet (Beta vulgaris L.). Field Crops Research, 151, 27-34.
Taleghani, D., Rajabi, A., Hemayati, S. S., & Saremirad, A. (2022). Improvement and selection for drought-tolerant sugar beet (Beta vulgaris L.) pollinator lines. Results in Engineering, 13, 100367.
Weeden, B. R. (2000). Potential of sugar beet on the Atherton tableland. A Report for the Rural Industries Research and Development Corporation, Project No DAQ-211A
Yolcu, S., Alavilli, H., Ganesh, P., Asif, M., Kumar, M., & Song, K. (2021). An insight into the abiotic stress responses of cultivated beets (Beta vulgaris L.). Plants, 11(1), 12.
Wheat
Barlow, K. M., Christy, B. P., O’leary, G. J., Riffkin, P. A., & Nuttall, J. G. (2015). Simulating the impact of extreme heat and frost events on wheat crop production: A review. Field crops research, 171, 109-119.
FAO, F. (2018). The impact of disasters and crises on agriculture and food security. Report.
Farooq, M., Bramley, H., Palta, J. A., & Siddique, K. H. (2011). Heat stress in wheat during reproductive and grain-filling phases. Critical Reviews in Plant Sciences, 30(6), 491-507.
Frederiks, T. M., Christopher, J. T., & Borrell, A. K. (2008). Low temperature adaption of wheat post head-emergence in northern Australia.
Kulkarni, M., Soolanayakanahally, R., Ogawa, S., Uga, Y., Selvaraj, M. G., & Kagale, S. (2017). Drought response in wheat: key genes and regulatory mechanisms controlling root system architecture and transpiration efficiency. Frontiers in chemistry, 5, 106.
Li, P. H. (Ed.). (2012). Plant cold hardiness and freezing stress: Mechanisms and crop implications (Vol. 2). Elsevier.
Marček, T., Hamow, K. Á., Végh, B., Janda, T., & Darko, E. (2019). Metabolic response to drought in six winter wheat genotypes. PLoS one, 14(2), e0212411.
Marcellos, H., & Single, W. V. (1972). The influence of cultivar, temperature and photoperiod on post-flowering development of wheat. Australian Journal of Agricultural Research, 23(4), 533-540.
Martre, P., Wallach, D., Asseng, S., Ewert, F., Jones, J. W., Rötter, R. P., ... & Wolf, J. (2015). Multimodel ensembles of wheat growth: many models are better than one. Global change biology, 21(2), 911-925.
Modarresi, M., Mohammadi, V., Zali, A., & Mardi, M. (2010). Response of wheat yield and yield related traits to high temperature. Cereal Research Communications, 38(1), 23-31.
Pradhan, G. P., Prasad, P. V., Fritz, A. K., Kirkham, M. B., & Gill, B. S. (2012). Effects of drought and high temperature stress on synthetic hexaploid wheat. Functional Plant Biology, 39(3), 190-198.
Sarto, M. V. M., Sarto, J. R. W., Rampim, L., Rosset, J. S., Bassegio, D., da Costa, P. F., & Inagaki, A. M. (2017). Wheat phenology and yield under drought: a review. Australian Journal of Crop Science, 11(8), 941-946.
Wheeler, T. R., Batts, G. R., Ellis, R. H., Hadley, P., & Morison, J. I. L. (1996). Growth and yield of winter wheat (Triticum aestivum) crops in response to CO2 and temperature. The Journal of Agricultural Science, 127(1), 37-48.
Sugarcane
Adami, M., Rudorff, B. F. T., Freitas, R. M., Aguiar, D. A., Sugawara, L. M., & Mello, M. P. (2012). Remote sensing time series to evaluate direct land use change of recent expanded sugarcane crop in Brazil. Sustainability, 4(4), 574-585.
de Carvalho, A. L., Menezes, R. S. C., Nóbrega, R. S., de Siqueira Pinto, A., Ometto, J. P. H. B., von Randow, C., & Giarolla, A. (2015). Impact of climate changes on potential sugarcane yield in Pernambuco, northeastern region of Brazil. Renewable Energy, 78, 26-34.
Flack‐Prain, S., Shi, L., Zhu, P., da Rocha, H. R., Cabral, O., Hu, S., & Williams, M. (2021). The impact of climate change and climate extremes on sugarcane production. GCB Bioenergy, 13(3), 408-424.
Li, Y. R., & Yang, L. T. (2015). Sugarcane agriculture and sugar industry in China. Sugar Tech, 17(1), 1-8.
Narayanamoorthy, A. (2005). Economics of drip irrigation in sugarcane cultivation: Case study of a farmer from Tamil Nadu. Indian Journal of Agricultural Economics, 60(902-2016-66811).
Santos, D. L. D., & Sentelhas, P. C. (2012). Climate change scenarios and their impact on the water balance of sugarcane production areas in the State of São Paulo, Brazil. Revista Ambiente & Água, 7, 07-17.
Zhao, D., & Li, Y. R. (2015). Climate change and sugarcane production: potential impact and mitigation strategies. International Journal of Agronomy, 2015.
Canola
Aksouh-Harradj, N. M., Campbell, L. C., & Mailer, R. J. (2006). Canola response to high and moderately high temperature stresses during seed maturation. Canadian journal of plant science, 86(4), 967-980.
Assefa, Y., Prasad, P. V., Foster, C., Wright, Y., Young, S., Bradley, P., ... & Ciampitti, I. A. (2018). Major management factors determining spring and winter canola yield in North America. Crop Science, 58(1), 1-16.
Bushong, J. A., Griffith, A. P., Peeper, T. F., & Epplin, F. M. (2012). Continuous winter wheat versus a winter canola–winter wheat rotation. Agronomy Journal, 104(2), 324-330.
Champolivier, L., & Merrien, A. (1996). Effects of water stress applied at different growth stages to Brassica napus L. var. oleifera on yield, yield components and seed quality. European Journal of Agronomy, 5(3-4), 153-160.
Din, J., Khan, S. U., Ali, I., & Gurmani, A. R. (2011). Physiological and agronomic response of canola varieties to drought stress. J Anim Plant Sci, 21(1), 78-82.
Elferjani, R., & Soolanayakanahally, R. (2018). Canola responses to drought, heat, and combined stress: shared and specific effects on carbon assimilation, seed yield, and oil composition. Frontiers in plant science, 9, 1224.
Raymer, P. L. (2002). Canola: an emerging oilseed crop. Trends in new crops and new uses, 1, 122-126.
Wallace, G. E., & Huda, A. (2005). Using climate information to approximate the value at risk of a forward contracted canola crop. Australian Farm Business Management Journal, 2(1), 75-83.
Sunflower
Agüera, E., & de la Haba, P. (2021). Climate Change Impacts on Sunflower (Helianthus annus L.) Plants. Plants, 10(12), 2646.
Debaeke, P., Casadebaig, P., Flenet, F., & Langlade, N. (2017). Sunflower crop and climate change: vulnerability, adaptation, and mitigation potential from case-studies in Europe. OCL Oilseeds and fats crops and lipids, 24(1), 15-p.
García-Vila, M., Fereres, E., Prieto, M. H., Ruz, C., & Soriano, M. A. (2012). Sunflower. Crop yield response to water FAO Irrigation and Drainage, Paper, 66.
Gurkan, H., Ozgen, Y., Bayraktar, N., Bulut, H., & Yildiz, M. (2020). Possible impacts of climate change on sunflower yield in Turkey. In Agronomy-Climate Change & Food Security. IntechOpen.
Soybean
Bhardwaj, S. F. (1986). Consumptive use and water requirement of soybeans. Journal of irrigation and drainage engineering, 112(2), 157-163.
Cotrim, M. F., Gava, R., Campos, C. N. S., de David, C. H. O., Reis, I. D. A., Teodoro, L. P. R., & Teodoro, P. E. (2021). Physiological performance of soybean genotypes grown under irrigated and rainfed conditions. Journal of Agronomy and Crop Science, 207(1), 34-43.
Dros, J. M. (2004). Managing the Soy Boom: Two scenarios of soy production. Amsterdam: AIDEnvironment.
Jin, Z., Zhuang, Q., Wang, J., Archontoulis, S. V., Zobel, Z., & Kotamarthi, V. R. (2017). The combined and separate impacts of climate extremes on the current and future US rainfed maize and soybean production under elevated CO2. Global change biology, 23(7), 2687-2704.
Nielsen, R. L., & Christmas, E. (2002). Early season frost & low temperature damage to corn and soybean. Corny News Netw.
Sabagh, A. E., Hossain, A., Islam, M. S., Iqbal, M. A., Fahad, S., Ratnasekera, D., & Llanes, A. (2020). Consequences and mitigation strategies of heat stress for sustainability of soybean (Glycine max L. Merr.) production under the changing climate. Plant stress physiology.
Salassi, M. E., Musick, J. A., Heatherly, L. G., & Hamill, J. G. (1984). An economic analysis of soybean yield response to irrigation of Mississippi River Delta soils.
Specht, J. E., Chase, K., Macrander, M., Graef, G. L., Chung, J., Markwell, J. P., ... & Lark, K. G. (2001). Soybean response to water: a QTL analysis of drought tolerance. Crop science, 41(2), 493-509.
Wei, Y., Jin, J., Jiang, S., Ning, S., & Liu, L. (2018). Quantitative response of soybean development and yield to drought stress during different growth stages in the Huaibei Plain, China. Agronomy, 8(7), 97.
Table grapes
Costea, M. et al. (2019) ‘Assessment of climatic conditions as driving factors of wine aromatic compounds: a case study from Central Romania’, Theoretical and Applied Climatology, 137(1), pp. 239–254. Available at: https://doi.org/10.1007/s00704-018-2594-2
Ferrara, G. et al. (2022) ‘Effects of different winter pruning times on table grape vines performance and starch reserves to face climate changes’, Scientia Horticulturae, 305, p. 111385. Available at: https://doi.org/10.1016/j.scienta.2022.111385
Fraga, H. et al. (2020) ‘What Is the Impact of Heatwaves on European Viticulture? A Modelling Assessment’, Applied Sciences, 10(9), p. 3030. Available at: https://doi.org/10.3390/app10093030
Jones, G.V., Reid, R. and Vilks, A. (2012) ‘Climate, Grapes, and Wine: Structure and Suitability in a Variable and Changing Climate’, in P.H. Dougherty (ed.) The Geography of Wine: Regions, Terroir and Techniques. Dordrecht: Springer Netherlands, pp. 109–133. Available at: https://doi.org/10.1007/978-94-007-0464-0_7
Karimi, R. (2020) ‘Cold Hardiness Evaluation of 20 Commercial Table Grape (vitis Vinifera L.) Cultivars’, International Journal of Fruit Science, 20(3), pp. 433–450. Available at: https://doi.org/10.1080/15538362.2019.1651242
Karimi, R. and Ershadi, A. (2015) ‘Role of exogenous abscisic acid in adapting of “Sultana” grapevine to low-temperature stress’, Acta Physiologiae Plantarum, 37(8), p. 151. Available at: https://doi.org/10.1007/s11738-015-1902-z
Permanhani, M. et al. (2016) ‘Deficit irrigation in table grape: eco-physiological basis and potential use to save water and improve quality’, Theoretical and Experimental Plant Physiology, 28(1), pp. 85–108. Available at: https://doi.org/10.1007/s40626-016-0063-9
Rodríguez, J.C. et al. (2010) ‘Water use by perennial crops in the lower Sonora watershed’, Journal of Arid Environments, 74(5), pp. 603–610. Available at: https://doi.org/10.1016/j.jaridenv.2009.11.008
Venios, X. et al. (2020) ‘Grapevine Responses to Heat Stress and Global Warming’, Plants (Basel, Switzerland), 9(12), p. 1754. Available at: https://doi.org/10.3390/plants9121754
Annex E – Selection of climate models
Ayugi, B. et al. (2021) ‘Evaluation and projection of mean surface temperature using CMIP6 models over East Africa’, Journal of African Earth Sciences, 181, p. 104226. Available at: https://doi.org/10.1016/j.jafrearsci.2021.104226
Firpo, M.Â.F. et al. (2022) ‘Assessment of CMIP6 models’ performance in simulating present- day climate in Brazil’, Frontiers in Climate, 4. Available at: https://www.frontiersin.org/articles/10.3389/fclim.2022.948499 (Accessed: 24 November 2022).
Hamed, M.M., Nashwan, M.S. and Shahid, S. (2022) ‘Inter-comparison of historical simulation and future projections of rainfall and temperature by CMIP5 and CMIP6 GCMs over Egypt’, International Journal of Climatology, 42(8), pp. 4316–4332. Available at: https://doi.org/10.1002/joc.7468
Kim, Y.-H. et al. (2020) ‘Evaluation of the CMIP6 multi-model ensemble for climate extreme indices’, Weather and Climate Extremes, 29, p. 100269. Available at: https://doi.org/10.1016/j.wace.2020.100269
Masud, B. et al. (2021) ‘Means and Extremes: Evaluation of a CMIP6 Multi-Model Ensemble in Reproducing Historical Climate Characteristics across Alberta, Canada’, Water, 13(5), p.737. Available at: https://doi.org/10.3390/w13050737
Mesgari, E. et al. (2022) ‘Assessment of CMIP6 models’ performances and projection of precipitation based on SSP scenarios over the MENAP region’, Journal of Water and Climate Change, 13(10), pp. 3607–3619. Available at: https://doi.org/10.2166/wcc.2022.195
Mitra, A. (2021) ‘A Comparative Study on the Skill of CMIP6 Models to Preserve Daily Spatial Patterns of Monsoon Rainfall Over India’, Frontiers in Climate, 3. Available at: https://www.frontiersin.org/articles/10.3389/fclim.2021.654763 (Accessed: 25 November 2022).
Palmer, T.E. et al. (2022) ‘Performance based sub-selection of CMIP6 models for impact assessments in Europe’, Earth System Dynamics Discussions, pp. 1–45. Available at: https://doi.org/10.5194/esd-2022-31
Papalexiou, S.M. et al. (2020) ‘Robustness of CMIP6 Historical Global Mean Temperature Simulations: Trends, Long-Term Persistence, Autocorrelation, and Distributional Shape’, Earth’s Future, 8(10), p. e2020EF001667. Available at: https://doi.org/10.1029/2020EF001667
Ridder, N.N., Pitman, A.J. and Ukkola, A.M. (2021) ‘Do CMIP6 Climate Models Simulate Global or Regional Compound Events Skillfully?’, Geophysical Research Letters, 48(2), p.e2020GL091152. Available at: https://doi.org/10.1029/2020GL091152
Annex F – Climate adaptation practices
Oats
Chinnici, M.F. and Peterson, D.M. (1979) ‘Temperature and Drought Effects on Blast and Other Characteristics in Developing Oats 1’, Crop Science, 19(6), pp. 893–897. Djaman, K. et al. (2018) ‘Evapotranspiration, Grain Yield, and Water Productivity of Spring Oat (Avena sativa L.) under Semiarid Climate’, Agricultural Sciences, 09(09). Available at: https://doi.org/10.4236/as.2018.99083
Elarab, M. et al. (2015) ‘Estimating chlorophyll with thermal and broadband multispectral high resolution imagery from an unmanned aerial system using relevance vector machines for precision agriculture’, International Journal of Applied Earth Observation and Geoinformation, 43, pp. 32–42. Available at: https://doi.org/10.1016/j.jag.2015.03.017
Farkas, Z. et al. (2021) ‘CO2 Responses of Winter Wheat, Barley and Oat Cultivars under Optimum and Limited Irrigation’, Sustainability, 13(17). Available at: https://doi.org/10.3390/su13179931
Flores-Juárez, D.J. et al. (2020) ‘Inoculation of forage oats with arbuscular mycorrhizal fungi’, Revista Mexicana de Ciencias Agrícolas, 11(SPE24), pp. 191–199. Available at: https://doi.org/10.29312/remexca.v0i24.2369
Fonteyne, S. et al. (2019) ‘Conservation agriculture improves long-term yield and soil quality in irrigated maize-oats rotation’, Agronomy, 9(12), p. 845.
Gusta, L.V. and O’connor, B.J. (1987) ‘Frost tolerance of wheat, oats, barley, canola and mustard and the role of ice-nucleating bacteria’, Canadian Journal of Plant Science, 67(4), pp. 1155–1165.
Hetherington, S.D. and Auld, B.A. (2001) ‘Host range of Drechslera avenacea, a fungus with potential for use as a biological control agent of Avena fatua’, Australasian Plant Pathology, 30(3), p. 205. Available at: https://doi.org/10.1071/AP01020
Ivanovna, M.K. et al. (2018) ‘Moisture content of oats in different methods of soil processing and irrigation treatment on the frozen soils of Yakutia of the Russian Federation’, International Agricultural Journal, 3, Article 3.
Khairullina, A. et al. (2023) ‘Biocontrol Effect of Clonostachys rosea on Fusarium graminearum Infection and Mycotoxin Detoxification in Oat (Avena sativa’, Plants, 12(3), p. 500.
Macedo-Cruz, A. et al. (2011) ‘Digital image sensor-based assessment of the status of oat (Avena sativa L.) crops after frost damage’, Sensors, 11(6), pp. 6015–6036.
Martínez-López, J.A. et al. (2022) ‘Improving the Sustainability and Profitability of Oat and Garlic Crops in a Mediterranean Agro-Ecosystem under Water-Scarce Conditions’, Agronomy, 12(8).
McCosh J. et al. (2017) Upscaling of rainwater harvesting and conservation on communal crop and rangeland through integrated crop and livestock production for increased water use productivity. Available at: https://www.wrc.org.za/Wp-Content/Uploads/Mdocs/Tt%20712-17%20web.Pdf
Mushtaq, A. and Mehfuza, H. (2014) ‘A review on oat (Avena sativa L.) as a dual-purpose crop’, Scientific Research and Essays, 9(4), pp. 52–59.
Mut, Z., Akay, H. and Erbaş Köse, Ö.D. (2018) ‘Grain yield, quality traits and grain yield stability of local oat cultivars’, Journal of soil science and plant nutrition, 18(1), pp. 269–281.
Neumann, A., Schmidtke, K. and Rauber, R. (2007) ‘Effects of crop density and tillage system on grain yield and N uptake from soil and atmosphere of sole and intercropped pea and oat’, Field Crops Research, 100(2–3), pp. 285–293. Available at: https://doi.org/10.1016/j.fcr.2006.08.001
Oats Production in the Summer Rainfall Region (no date) Sensako. Available at: https://www.sensako.co.za/NewsArticle.aspx?id=32 (Accessed: 7 April 2023).
Reynolds, S.G. and Suttie, J.M. (eds) (2004) Fodder Oats: a world overview. Food and Agriculture Organization of the United Nations. Sánchez-Martín, J. et al. (2016) ‘Higher rust resistance and similar yield of oat landraces versus cultivars under high temperature and drought’, Agronomy for Sustainable Development, 37(1), p. 3. Available at: https://doi.org/10.1007/s13593-016-0407-5
Söderström, M. et al. (2015) ‘Modelling within-field variations in deoxynivalenol (DON) content in oats using proximal and remote sensing’, Precision Agriculture, 16(1), pp. 1–14. Available at: https://doi.org/10.1007/s11119-014-9373-6
Sojka, R.E. et al. (1997) ‘Subsoiling and surface tillage effects on soil physical properties and forage oat stand and yield’, Soil and Tillage Research, 40(3–4), pp. 125–144. Available at: https://doi.org/10.1016/S0167-1987(96)01075-6
Solano-Sosa, M.Z. et al. (2022) ‘Forage evaluation based on oat on scenarios of intercrop and organic nutrition’, Agro Productividad [Preprint]. Available at: https://doi.org/10.32854/agrop.v15i7.2313
Tian, L. et al. (2022) ‘Effects of strip cropping with reducing row spacing and super absorbent polymer on yield and water productivity of oat (Avena sativa L.) under drip irrigation in Inner Mongolia, China’, Scientific Reports, 12, p. 10 1038 41598-022-15418-
V., H. et al. (2016) Oat forage. Feedipedia, a programme by INRAE, CIRAD, AFZ and FAO. Available at: https://www.feedipedia.org/node/500
Xie, H. et al. (2021) ‘Important physiological changes due to drought stress on oat’, Frontiers in Ecology and Evolution, 271(oats).
Xie, Hongying et al. (2021) ‘Important Physiological Changes Due to Drought Stress on Oat’, Frontiers in Ecology and Evolution, 9. 644726, p. 10 3389 2021 644726.
Xu, C. (2012) ‘A study on growth characteristics of different cultivars of oat (Avena sativa) in alpine region’, Acta Prataculturae Sinica, 21(2), pp. 280–285.
Zhang, Y. et al. (2019) ‘Optimized sowing time windows mitigate climate risks for oats production under cool semi-arid growing conditions’, Agricultural and Forest Meteorology, 266, pp. 184–197.
Zorovski, P. (2021) ‘Development, Productivity and Quality of Naked Oat Grain After Treatment with Biofertilizer in the Conditions of Organic Agriculture’, Scientific Papers Agronomy, 64(1).
Corn
Abreu, V.M. de et al. (2014) ‘Physiological performance and expression of isozymes in maize seeds subjected to water stress’, Journal of Seed Science, 36, pp. 40–47.
Abreu, V.M. de et al. (2019) ‘Combining Ability and Heterosis of Maize Genotypes under Water Stress during Seed Germination and Seedling Emergence’, Crop Science, 59(1), pp. 33–43. Available at: https://doi.org/10.2135/cropsci2018.03.0161
Abreu, V.M.D. et al. (2017) ‘Indirect Selection for Drought Tolerance in Maize Through Agronomic and Seeds Traits’, Revista Brasileira de Milho e Sorgo, 16(2). Available at: https://doi.org/10.18512/1980-6477/rbms.v16n2p287-296
Altieri, M.A., Funes-Monzote, F.R. and Petersen, P. (2012) ‘Agroecologically efficient agricultural systems for smallholder farmers: Contributions to food sovereignty’, Agronomy for Sustainable Development, 32(1), pp. 1–13. Available at: https://doi.org/10.1007/s13593-011-0065-6
Ananthi, T. and Amanullah, M.M. (2017) ‘A Review On Maize- Legume Intercropping For Enhancing The Productivity And Soil Fertility For Sustainable Agriculture’, Advances in Environmental Biology, 11(5), pp. 49–63.
Araus, J.L., Serret, M.D. and Edmeades, G. (2012) ‘Phenotyping maize for adaptation to drought’, Frontiers in Physiology, 3. Available at: https://www.frontiersin.org/articles/10.3389/fphys.2012.00305
Awika, J.M. (2011) ‘Major cereal grains production and use around the world’, in Advances in cereal science: implications to food processing and health promotion. American Chemical Society, pp. 1–13.
Balusamy, A., Udayasoorian, C. and Jayabalakrishnan, R. (2019) ‘Effect of Subsurface Drainage System on Maize Growth, Yield and Soil Quality’, International Journal of Current Microbiology and Applied Sciences, 8(02), pp. 1206–1215. Available at: https://doi.org/10.20546/ijcmas.2019.802.140
Barutcular, C. et al. (2016) ‘Evaluation of maize hybrids to terminal drought stress tolerance by defining drought indices’, Journal of Experimental Biology and Agricultural Sciences, 4(6), pp. 610–616. Available at: https://doi.org/10.18006/2016.4(Issue6).610.616
Baum, M.E., Archontoulis, S.V. and Licht, M.A. (2019) ‘Planting Date, Hybrid Maturity, and Weather Effects on Maize Yield and Crop Stage’, Agronomy Journal, 111(1), pp. 303–313. Available at: https://doi.org/10.2134/agronj2018.04.0297
Bolaños, J. and Edmeades, G.O. (1993) ‘Eight cycles of selection for drought tolerance in lowland tropical maize. I. Responses in grain yield, biomass, and radiation utilization’, Field Crops Research, 31(3–4), pp. 233–252.
Botha, J. et al. (2015) ‘Rainwater harvesting and conservation tillage increase maize yields in South Africa’, Water Resources and Rural Development, 6, p. 10 1016 2015 04 001.
Bowles, T.M. et al. (2020) ‘Long-Term Evidence Shows that Crop-Rotation Diversification Increases Agricultural Resilience to Adverse Growing Conditions in North America’, One Earth, 2(3), pp. 284–293. Available at: https://doi.org/10.1016/j.oneear.2020.02.007
Butler, E.E., Mueller, N.D. and Huybers, P. (2018) ‘Peculiarly pleasant weather for US maize’, Proceedings of the National Academy of Sciences, 115(47), pp. 11935–11940.
Camp, C.R. et al. (2001) ‘Irrigation and nitrogen management with a site-specific center pivot’, in Proc. of the Third Europ. Conf. on Prec. Agric. Montpellier, France, Agro Montpellier.
Camp, C.R., Bauer, P.J. and Busscher, W.J. (1999) ‘Evaluation of no-tillage crop production with subsurface drip irrigation on soils with compacted layers’, Transactions of the ASAE, 42(4), p. 911.
Cardozo, N.P., Oliveira Bordonal, R. and Scala, N. (2018) ‘Sustainable intensification of sugarcane production under irrigation systems, considering climate interactions and agricultural efficiency’, Journal of Cleaner Production, 204, pp. 861–871. Available at: https://doi.org/10.1016/j.jclepro.2018.09.004
Chen, B., Gramig, B.M. and Yun, S.D. (2021) ‘Conservation tillage mitigates drought-induced soybean yield losses in the US Corn Belt’, Q Open, 1(1), p. qoab007. Available at: https://doi.org/10.1093/qopen/qoab007
Chen, G. and Weil, R.R. (2011) ‘Root growth and yield of maize as affected by soil compaction and cover crops’, Soil and Tillage Research, 117, pp. 17–27.
Coughenour, C.M. and Chamala, S. (eds) (2000) Conservation Tillage and Cropping Innovation. Iowa State University Press. Available at: https://doi.org/10.1002/9780470290149
Dahal, S. et al. (2020) ‘Degradability of biodegradable soil moisture sensor components and their effect on maize (Zea mays L.) growth’, Sensors, 20(21), p. 6154.
Easson, D.L. and Fearnehough, W. (2000) ‘Effects of plastic mulch, sowing date and cultivar on the yield and maturity of forage maize grown under marginal climatic conditions in Northern Ireland’, Grass and Forage Science, 55(3), pp. 221–231.
El-Husseini, M.M., El-Heneidy, A.H. and Awadallah, K.T. (2018) ‘Natural enemies associated with some economic pests in Egyptian agro-ecosystems’, Egyptian Journal of Biological Pest Control, 28(1), p. 78. Available at: https://doi.org/10.1186/s41938-018-0081-9
Elmetwalli, A.M.H. (2008) ‘Remote Sensing as a Precision Farming Tool in the Nile Valley’, Egypt [Preprint]. Available at: http://dspace.stir.ac.uk/handle/1893/844
El-Sappagh, I.A., Khalil, A.E.H. and EL-AMIR, S. (2022) ‘Efficacy of Some Synthetic Insecticides and Botanical oils against Fall Armyworm, Spodoptera frugiperda (Lepidoptera: Noctuidae’, on Maize in Egypt. Egyptian Academic Journal of Biological Sciences, F. Toxicology & Pest Control, 14(1), pp. 99–107.
Eneji, A.E. et al. (2008) ‘Growth and Nutrient Use in Four Grasses Under Drought Stress as Mediated by Silicon Fertilizers’, Journal of Plant Nutrition, 31(2), pp. 355–365. Available at: https://doi.org/10.1080/01904160801894913
Ferreira, F. (no date) Change in cropping practices under centre pivot irrigation, Change in cropping practices under centre pivot irrigation. Available at: https://www.grainsa.co.za/change-in-cropping-practices-under-centre-pivot-irrigation (Accessed: 5 April 2023).
Filintas, A. (2021) ‘Soil moisture depletion modelling using a TDR multi-sensor system, GIS, soil analyzes, precision agriculture and remote sensing on maize for improved irrigation- fertilization decisions’, Engineering Proceedings, 9(1), p. 36.
Flores-Sanchez, D. et al. (2013) ‘Exploring maize-legume intercropping systems in Southwest Mexico’, Agroecology and Sustainable Food Systems, 37(7), pp. 739–761.
Goswami, J. et al. (2019) ‘RAPID IDENTIFICATION OF ABIOTIC STRESS (FROST) IN IN- FILED MAIZE CROP USING UAV REMOTE SENSING’, The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLII-3-W6, pp. 467– 471. Available at: https://doi.org/10.5194/isprs-archives-XLII-3-W6-467-2019
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Grewal, S.S. et al. (1989) ‘Rainwater harvesting for the management of agricultural droughts in the foothills of northern India’, Agricultural Water Management, 16(4), pp. 309–322. Available at: https://doi.org/10.1016/0378-3774(89)90028-0
Hallauer, A.R. and Carena, M.J. (2009) ‘Maize’, in Cereals. New York, NY: Springer, pp. 3–98.
Hillnhütter, C. et al. (2011) ‘Remote sensing to detect plant stress induced by Heterodera schachtii and Rhizoctonia solani in sugar beet fields’, Field Crops Research, 122(1), pp. 70–77. Available at: https://doi.org/10.1016/j.fcr.2011.02.007
Hörbe, T.A.N. et al. (2013) ‘Optimization of corn plant population according to management zones in Southern Brazil’, Precision Agriculture, 14(4), pp. 450–465. Available at: https://doi.org/10.1007/s11119-013-9308-7
Huynh, H.T. et al. (2019) ‘Influences of soil tillage, irrigation and crop rotation on maize biomass yield in a 9-year field study in Müncheberg, Germany’, Field Crops Research, 241, p. 107565. Available at: https://doi.org/10.1016/j.fcr.2019.107565
Idowu, O.J. et al. (2019) ‘Short-term conservation tillage effects on corn silage yield and soil quality in an irrigated, arid agroecosystem’, Agronomy, 9(8), p. 455.
Jat, H.S. et al. (2019) ‘Re-designing irrigated intensive cereal systems through bundling precision agronomic innovations for transitioning towards agricultural sustainability in North-West India’, Scientific Reports, 9(1). Available at: https://doi.org/10.1038/s41598-019-54086-1
Jat, R.S. and Ahlawat, I.P.S. (2006) ‘Direct and Residual Effect of Vermicompost, Biofertilizers and Phosphorus on Soil Nutrient Dynamics and Productivity of Chickpea-Fodder Maize Sequence’, Journal of Sustainable Agriculture, 28(1), pp. 41–54. Available at: https://doi.org/10.1300/J064v28n01_05
Jeffries, G.R. et al. (2020) ‘Mapping sub-field maize yields in Nebraska, USA by combining remote sensing imagery, crop simulation models, and machine learning’, Precision Agriculture, 21(3), pp. 678–694.
Jug, D. et al. (2007) ‘Influence of different soil tillage systems on yield of maize’, Cereal Research Communications, 35(2), pp. 557–560.
Lamm, F.R. and Trooien, T.P. (2003) ‘Subsurface drip irrigation for corn production: A review of 10 years of research in Kansas’, Irrigation Science, 22(3–4), pp. 195–200. Available at: https://doi.org/10.1007/s00271-003-0085-3
Linker, R. and Kisekka, I. (2017) ‘Model-Based Deficit Irrigation of Maize in Kansas’, Transactions of the ASABE, 60(6), pp. 2011–2022. Available at: https://doi.org/10.13031/trans.12341
Lobell, D.B. and Asner, G.P. (2003) ‘Climate and management contributions to recent trends in US agricultural yields’, Science, 299(5609), pp. 1032–1032.
Lobell, D.B., Deines, J.M. and Tommaso, S.D. (2020) ‘Changes in the drought sensitivity of US maize yields’, Nature Food, 1(11), pp. 729–735.
Lourdes Corrêa Figueiredo, M. et al. (2015) ‘Biological control with Trichogramma pretiosum increases organic maize productivity by 19.4%’, Agronomy for Sustainable Development, 35(3), pp. 1175–1183. Available at: https://doi.org/10.1007/s13593-015-0312-3
Maia, S.M.F. et al. (2019) ‘Combined effect of intercropping and minimum tillage on soil carbon sequestration and organic matter pools in the semiarid region of Brazil’, Soil Research, 57(3), pp. 266–275. Available at: https://doi.org/10.1071/SR17336
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Martins, M.A. et al. (2018) ‘Improving drought management in the Brazilian semiarid through crop forecasting’, Agricultural Systems, 160, pp. 21–30. Available at: https://doi.org/10.1016/j.agsy.2017.11.002
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Moeletsi, M. (2017) ‘Mapping of Maize Growing Period over the Free State Province of South Africa: Heat Units Approach’, Advances in Meteorology, pp. 1–11.
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Moitzi, G., Sattler, E. and Wagentristl, H. (2021) ‘Effect of cover crop, slurry application with different loads and tire inflation pressures on tire track depth, soil penetration resistance and maize yield’, Agriculture, 11(7), p. 641.
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Muhumed, M. et al. (2014) ‘Effects of drip irrigation frequency, fertilizer sources and their interaction on the dry matter and yield components of sweet corn’, AJCS, [online, 8(2), pp. 223–231.
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Samie, A.-E. et al. (2022) ‘Effect of foliar spray by different ascorbic acid and zinc concentration on yield and yield components of maize under heat stress’, Fayoum Journal of Agricultural Research and Development, 36(1), pp. 21–33.
Sandhu, D. and Irmak, S. (2019) ‘Performance of AquaCrop model in simulating maize growth, yield, and evapotranspiration under rainfed, limited and full irrigation’, Agricultural Water Management, 223, p. 105687. Available at: https://doi.org/10.1016/j.agwat.2019.105687
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