24 research outputs found

    Modelling crop yields and climate risk under limited climate data conditions

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    Agricultural management needs relevant climate information to reduce the climate uncertainty and support crucial management decisions. Risk profiles of modelled crop yields (cumulative probability curves) are effective tools for summarising long-term yield variability, exploring the benefit and limitations of agricultural management decisions and serve to quantify the impact of future climate conditions. However, modelling reliable crop yield and risk profiles requires continuous, accurate, and long-term (>100 years) local weather records for rainfall, temperature, and solar radiation, which are not always available. This study aimed to systematically assess spatial and temporal factors that limit the accuracy of risk profile of modelled crop yields. The specific objectives were (1) to analyse if and to what degree short time series of weather data can be used to provide reliable risk profiles, (2) to test how simple adjustments of high-quality local data can be used to extrapolate risk profiles across broad climatic regions, and (3) to address a combination of sparse spatial coverage of climate data and short daily weather observations. Here we focused on the Australian grain-belt selected on the basis of the availability to high-quality, long-term climate data, widely used and calibrated process-based crop model (APSIM, Agricultural Production Systems sIMulator). To examine the sensitivity of risk profiles of modelled crop yields to the temporal coverage of the climate data, 15 wheat-growing sites were selected based on their proximity to weather stations with high-quality daily weather records for the last 100 years (baseline period). Risk profiles were constructed using variable temporal coverages and compared with risk profiles obtained for the baseline period. Results indicated a decline of modelled wheat grain yields, particularly for the last three decades. They also highlight the interactions between model complexity and data demand. The sensitivity of the risk profiles to record length was increased in models accounting for severe frost and heat events. The second research objective of this study addresses spatial extrapolation and explores to what extent a simple method for adjusting daily weather data using seasonal and monthly factors could produce robust estimates of risk profiles at a continental scale. Adjustment factors were calculated as the difference in long-term average of a given climate variable between 49 test sites and the reference site. Risk profiles modelled with observed weather data were compared with those modelled with adjusted data. Simple adjustments of both precipitation and temperatures produced reliable risk profiles in 80% of the sites. This study implies that for regions with limited availability of high-quality climate data, simple scaling of climate inputs can provide basic climate data for modelling and generating robust spatial patterns of risk profiles of crop yield. The third objective addresses the realistic scenario of using modern, process-based crop models, which are data hungry, in data sparse environments. Models that can capture combinations of potential climate and management impacts on food production require complex climate data that are either not available or difficult to access at high spatial detail and/or temporal extent for many parts of the world. Here, we assess the sensitivity of the risk profile accuracy to the temporal coverage of the climate data combined with spatial adjustments of daily weather data for risk profile modelling purposes. In this case, adjustment factors were determined using a variable temporal coverage at every study site. Risk profiles were modelled using observed and adjusted weather data covering different periods. Results indicated that although adjustment factors are very sensitive to the record length of the climate data, it was possible to produced reliable risk profiles with only 10-30 years of climate data. This research has increased our understanding of the sensitivity of risk profiles to the temporal and spatial aspects of climate data availability. It highlights the usefulness of risk profiles to characterise spatial and temporal patterns of yield and will help to improve agricultural management under climate uncertainty.Thesis (Ph.D.) (Research by Publication) -- University of Adelaide, School of Biological Sciences, 201

    Projected impacts of sowing date and cultivar choice on the timing of heat and drought stress in spring barley grown along a European transect

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    Publisher Copyright: © 2022Barley is one of the most important cereals for animal and human consumption. Barley heading and grain filling are especially vulnerable to heat and drought stress, which are projected to increase in the future. Therefore, site-specific adaptation options, like cultivar choice or shifting sowing dates, will be necessary. Using a global climate model ensemble and a phenology model we projected spring barley heading and maturity dates for 2031–50 for climatically contrasting sites: Helsinki (Finland), Dundee (Scotland) and Zaragoza (Spain). We compared the projected future heading and maturity dates with the baseline period (1981–2010) and described corresponding heat and drought stress conditions and how they were affected by adaptation options, i.e. shifting the sowing date by + /- 10–20 days, choosing early or late heading cultivars or combining both adaptation options, with agroclimatic indicators. At all sites and sowing dates, heading and maturity in 2031–50 occurred earlier (up to three weeks with earliest sowing) than in the baseline period. Along the European transect, the projected heading and grain filling periods were hotter than under baseline conditions but advancing heading alleviated heat stress notably. Different indicators signaled more severe drought conditions for 2031–50. At Helsinki, delayed heading periods were exposed to less drought stress, likely because the typical early summer droughts were avoided. At Zaragoza, fewer, yet more intense, rainfall events occurred during grain filling of the early cultivars. Only under scenario RCP4.5, heading and grain filling periods at Dundee were slightly wetter for the early cultivars. Our study provides a unique overview of agroclimatic conditions for heading and grain filling periods projected for 2031–50 along a climatic transect and quantifies the effects of different adaptations for spring barley. The approach can be extended by coupling the agroclimatic indicators with crop modelling.Peer reviewe

    Evidence for increasing global wheat yield potential

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    Wheat is the most widely grown food crop, with 761 Mt produced globally in 2020. To meet the expected grain demand by mid-century, wheat breeding strategies must continue to improve upon yield-advancing physiological traits, regardless of climate change impacts. Here, the best performing doubled haploid (DH) crosses with an increased canopy photosynthesis from wheat field experiments in the literature were extrapolated to the global scale with a multi-model ensemble of process-based wheat crop models to estimate global wheat production. The DH field experiments were also used to determine a quantitative relationship between wheat production and solar radiation to estimate genetic yield potential. The multi-model ensemble projected a global annual wheat production of 1050 ± 145 Mt due to the improved canopy photosynthesis, a 37% increase, without expanding cropping area. Achieving this genetic yield potential would meet the lower estimate of the projected grain demand in 2050, albeit with considerable challenges.Fil: Guarin, Jose Rafael. National Aeronautics and Space Administration; Estados Unidos. Columbia University; Estados Unidos. Florida State University; Estados UnidosFil: Martre, Pierre. Institut Agro Montpellier SupAgro; FranciaFil: Ewert, Frank. Universitat Bonn; Alemania. Leibniz Centre for Agricultural Landscape Research; AlemaniaFil: Webber, Heidi. Universitat Bonn; Alemania. Leibniz Centre for Agricultural Landscape Research; AlemaniaFil: Dueri, Sibylle. Institut Agro Montpellier SupAgro; FranciaFil: Calderini, Daniel Fernando. Universidad Austral de Chile; ChileFil: Reynolds, Matthew. International Maize and Wheat Improvement Center ; MéxicoFil: Molero, Gemma. KWS; FranciaFil: Miralles, Daniel Julio. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura. Universidad de Buenos Aires. Facultad de Agronomía. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura; ArgentinaFil: Garcia, Guillermo. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura. Universidad de Buenos Aires. Facultad de Agronomía. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura; ArgentinaFil: Slafer, Gustavo Ariel. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura. Universidad de Buenos Aires. Facultad de Agronomía. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura; Argentina. Universitat de Lleida; España. Institució Catalana de Recerca i Estudis Avancats; EspañaFil: Giunta, Francesco. Consiglio Nazionale Delle Ricerche. Istituto Di Scienze Dell Atmosfera E del Clima.; ItaliaFil: Pequeno, Diego N.L.. International Maize and Wheat Improvement Center; MéxicoFil: Stella, Tommaso. Universitat Bonn; Alemania. Leibniz Centre for Agricultural Landscape Research; AlemaniaFil: Ahmed, Mukhtar. University Of Pakistan; PakistánFil: Alderman, Phillip D.. Oklahoma State University; Estados UnidosFil: Basso, Bruno. Michigan State University; Estados UnidosFil: Berger, Andres G.. Instituto Nacional de Investigacion Agropecuaria;Fil: Bindi, Marco. Università degli Studi di Firenze; ItaliaFil: Bracho-Mujica, Gennady. Universität Göttingen; AlemaniaFil: Cammarano, Davide. Purdue University; Estados UnidosFil: Chen, Yi. Chinese Academy of Sciences; República de ChinaFil: Dumont, Benjamin. Université de Liège; BélgicaFil: Rezaei, Ehsan Eyshi. Leibniz Institute Of Plant Genetics And Crop Plant Research.; AlemaniaFil: Fereres, Elias. Universidad de Córdoba; EspañaFil: Ferrise, Roberto. Michigan State University; Estados UnidosFil: Gaiser, Thomas. Universitat Bonn; AlemaniaFil: Gao, Yujing. Florida State University; Estados UnidosFil: Garcia Vila, Margarita. Universidad de Córdoba; EspañaFil: Gayler, Sebastian. Universidad de Hohenheim; Alemani

    Evidence for increasing global wheat yield potential

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    Wheat is the most widely grown food crop, with 761 Mt produced globally in 2020. To meet the expected grain demand by mid-century, wheat breeding strategies must continue to improve upon yield-advancing physiological traits, regardless of climate change impacts. Here, the best performing doubled haploid (DH) crosses with an increased canopy photosynthesis from wheat field experiments in the literature were extrapolated to the global scale with a multi-model ensemble of process-based wheat crop models to estimate global wheat production. The DH field experiments were also used to determine a quantitative relationship between wheat production and solar radiation to estimate genetic yield potential. The multi-model ensemble projected a global annual wheat production of 1050 +/- 145 Mt due to the improved canopy photosynthesis, a 37% increase, without expanding cropping area. Achieving this genetic yield potential would meet the lower estimate of the projected grain demand in 2050, albeit with considerable challenges

    A high-yielding traits experiment for modeling potential production of wheat: field experiments and AgMIP-Wheat multi-model simulations

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    Grain production must increase by 60% in the next four decades to keep up with the expected population growth and food demand. A significant part of this increase must come from the improvement of staple crop grain yield potential. Crop growth simulation models combined with field experiments and crop physiology are powerful tools to quantify the impact of traits and trait combinations on grain yield potential which helps to guide breeding towards the most effective traits and trait combinations for future wheat crosses. The dataset reported here was created to analyze the value of physiological traits identified by the International Wheat Yield Partnership (IWYP) to improve wheat potential in high-yielding environments. This dataset consists of 11 growing seasons at three high-yielding locations in Buenos Aires (Argentina), Ciudad Obregon (Mexico), and Valdivia (Chile) with the spring wheat cultivar Bacanora and a high-yielding genotype selected from a doubled haploid (DH) population developed from the cross between the Bacanora and Weebil cultivars from the International Maize and Wheat Improvement Center (CIMMYT). This dataset was used in the Agricultural Model Intercomparison and Improvement Project (AgMIP) Wheat Phase 4 to evaluate crop model performance when simulating high-yielding physiological traits and to determine the potential production of wheat using an ensemble of 29 wheat crop models. The field trials were managed for non-stress conditions with full irrigation, fertilizer application, and without biotic stress. Data include local daily weather, soil characteristics and initial soil conditions, cultivar information, and crop measurements (anthesis and maturity dates, total above-ground biomass, final grain yield, yield components, and photosynthetically active radiation interception). Simulations include both daily in-season and end-of-season results for 25 crop variables simulated by 29 wheat crop models

    Modeling the multi-functionality of African savanna landscapes under global change

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    Various recent publications have indicated that accelerated global change and its negative impacts on terrestrial ecosystems in Southern Africa urgently demand quantitative assessment and modelling of a range of ecosystem services on which rural communities depend. Information is needed on how these Ecosystem Services (ES) can be enhanced through sustainable land management interventions and enabling policies. Yet, it has also been claimed that, to date, the required system analyses, data and tools to quantify important interactions between biophysical and socio-economic components, their resilience and ability to contribute to livelihood needs do not exist. We disagree, but acknowledge that building an appropriate integrative modelling framework for assessing the multi-functionality of savanna landscapes is challenging. Yet, in this Letter-to-the-Editor, we show that a number of suitable modelling components and required data already exist and can be mobilized and integrated with emerging data and tools to provide answers to problem-driven questions posed by stakeholders on land management and policy issues.German Federal Ministry of Education and Researchhttps://onlinelibrary.wiley.com/journal/1099145xhj2022Zoology and Entomolog

    Simple scaling of climate inputs allows robust extrapolation of modelled wheat yield risk at a continental scale

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    Climate change increases variability and uncertainty of crop performance. Process-based crop growth models represent the complex spatio-temporal interactions between plants, atmosphere, and soils and enable realistic climate risk assessments of future crop yield. But they require continuous, detailed daily weather data. Probability distributions of crop model results provide risk profiles of yield and serve to assess the impacts of long-term climate variability and change on crop yields. This paper tests to what extent a simple method for adjusting daily weather data using seasonal and monthly factors can produce robust estimates of risk profiles at a continental scale. We examined the predictability of risk profiles of modelled wheat grain yield across the Australian grain belt. Snowtown, in the middle of the South Australian grains belt (33.8°S, 138.2°E) was selected as the reference site, and 49 wheat-growing sites spanning from 23.5 to 42.8°S of latitude and 115–151.8°E of longitude were used for testing the adjustments of precipitation, maximum and minimum temperatures and global solar radiation. Adjustment factors were calculated as the difference in long-term average of a given climate variable between a test site and the reference site. For each test site, we compared risk profiles modelled with observed weather data with step-wise adjusted weather data. Simple adjustments of both rainfall and temperatures produced good matching of risk profiles (root mean square error, RMSE < 0.5 t/ha) in 80% of the sites. Adding the adjustment of the temperatures – with monthly factors- and solar radiation improved the match of risk profiles in the most climate-contrasting sites. In regions with limited availability of high-quality climate data, simple scaling of climate inputs used in this study can provide basic climate data for modelling and generating robust risk profiles of crop yield.Gennady Bracho-Mujica, Peter T. Hayman, Victor O. Sadras, Bertram Ostendor

    Salinity Constraints for Small-Scale Agriculture and Impact on Adaptation in North Aceh, Indonesia

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    We investigated the perceived effects of salinity on farming practices, income, and challenges for crop production in Blang Nibong village in North Aceh, Indonesia. We surveyed 120 smallholder farmers chosen in consultation with local leaders considering their agricultural activities and salinity susceptibility. Farmers’ perceptions of major crop production constraints (e.g., salinity) and potential adaptation strategies were assessed using open and closed questions. The study revealed that farmers in the study region primarily grew rain-fed rice using traditional monoculture. Salinity was identified as the primary crop production constraint by all respondents, resulting in plant mortality, decreased soil health and water quality, limited plant growth, and low yields. Additionally, salinity has reduced the arable area (>0.5 ha), resulting in lower total production. The implications of the salinity were further corroborated by the low farmers’ income. In fact, farming activities are not contributing positively to farmers’ income as the results revealed off-farm activities (77%) as the main source of income. Based on the farmer’s current activities to overcome salinity problems on their farms, they were clustered into adaptive and non-adaptive farmers. The non-adaptive group prefers to convert their land to pasture (81%), whereas the adaptive group prefers to improve the irrigation system (77%)

    Agronomic and Physiological Traits Response of Three Tropical Sorghum (<i>Sorghum bicolor</i> L.) Cultivars to Drought and Salinity

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    Sorghum holds the potential for enhancing food security, yet the impact of the interplay of water stress and salinity on its growth and productivity remains unclear. To address this, we studied how drought and salinity affect physiological traits, water use, biomass, and yield in different tropical sorghum varieties, utilizing a functional phenotyping platform, Plantarray. Cultivars (Kuali, Numbu, Samurai2) were grown under moderate and high salinity, with drought exposure at booting stage. Results showed that Samurai2 had the most significant transpiration reduction under moderate and high salt (36% and 48%) versus Kuali (22% and 42%) and Numbu (19% and 16%). Numbu reduced canopy conductance (25% and 15%) the most compared to Samurai2 (22% and 33%) and Kuali (8% and 35%). In the drought*salinity treatment, transpiration reduction was substantial for Kuali (54% and 57%), Samurai2 (45% and 60%), and Numbu (29% and 26%). Kuali reduced canopy conductance (36% and 53%) more than Numbu (36% and 25%) and Samurai2 (33% and 49%). Biomass, grain yield, and a-100 grain weight declined in all cultivars under both salinity and drought*salinity, and Samurai2 was most significantly affected. WUEbiomass significantly increased under drought*salinity. Samurai2 showed reduced WUEgrain under drought*salinity, unlike Kuali and Numbu, suggesting complex interactions between water limitation and salinity in tropical sorghum
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