Predicting optimum crop designs using crop models and seasonal climate forecasts

Rodrı́guez, Daniel; Voil, Peter de; Hudson, Debra; Brown, Jaclyn N.; Hayman, Peter; Marrou, Hélène; Meinke, Holger

doi:10.1038/s41598-018-20628-2

Cited by 70 publications

(37 citation statements)

References 39 publications

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“…Imperfect forecasts assessed are either operational (Carberry et al 2000;Wang et al 2009a) or experimental (Crean et al 2015) or both (McIntosh et al 2005;Rodriguez et al 2018). Perfect forecasts, which remove uncertainty about likely seasonal conditions, are often assessed alongside operational forecasts (McIntosh et al 2007;Rodriguez et al 2018). This approach tends to evaluate value across a wide gap of forecast skill with low skill levels of operational forecasts compared with 100 per cent skill from perfect forecasts.…”

Section: Value Of Incremental Forecast Skill Improvementmentioning

confidence: 99%

Insights into the value of seasonal climate forecasts to agriculture

Darbyshire

Crean

Cashen

et al. 2020

Aus J Agri & Res Econ

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Seasonal climate forecasts (forecasts) aim to reduce climate-related productivity risk by helping farmers make decisions that minimise losses in poor years and maximise profits in good years. Most Australian forecast valuations have focused on fertiliser decisions to wheat operations, and few assessments have evaluated the benefit of incremental improvements of forecast skill. These gaps have limited our understanding of forecast value to the broader agriculture sector and the benefit of investments to improve forecast skill. To address these gaps, we consistently assessed forecast value for seven Australian case studies (southern grains, northern grains, southern beef, northern beef, lamb, cotton, and sugar). We implemented a three-stage methodology which consisted of engagement with industry practitioners; modelling production under different climatic and environmental conditions; and economic modelling to evaluate forecast value for eleven levels of forecast skill. Our results show that forecast value was often low and highly variable. Value was found to vary based on forecast attributes (forecast skill, resolution and state), industry application and prevailing conditions (environmental and market). This is the first Australian valuation study where the same methodological approach was applied across multiple industries, incremental improvements in skill were valued, and prevailing conditions were explicitly evaluated for impact on value.

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Section: Value Of Incremental Forecast Skill Improvementmentioning

confidence: 99%

Insights into the value of seasonal climate forecasts to agriculture

Darbyshire

Crean

Cashen

et al. 2020

Aus J Agri & Res Econ

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“…For instance, the Australian Bureau of Meteorology produced twice-weekly weather forecasts for a period of 270 days, with a dynamic model called POAMA (http://poama.bom.gov.au/info/poama-2.html) [41][42][43]. Such seasonal forecasts have been used to look at management strategies of crops [44][45][46]. To improve these forecasts, which use on a grid of about 250 km, the Bureau of Meteorology is now proposing seasonal forecasts (ACCESS-S) based on ACCESS (Australian Community Climate and Earth System Simulator; http://www.bom.gov.au/australia/charts/about/about_access.shtml) using a 60 km grid.…”

Section: Should Eastern Australian Wheat Producers Adapt Their Decisimentioning

confidence: 99%

The Value of Tactical Adaptation to El Niño–Southern Oscillation for East Australian Wheat

Zheng

Chapman

Chenu

2018

Climate

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El Niño–Southern Oscillation strongly influences rainfall and temperature patterns in Eastern Australia, with major impacts on frost, heat, and drought stresses, and potential consequences for wheat production. Wheat phenology is a key factor to adapt to the risk of frost, heat, and drought stresses in the Australian wheatbelt. This study explores broad and specific options to adapt wheat cropping systems to El Niño–Southern Oscillation, and more specifically, to the Southern Oscillation Index (SOI) phases ahead of the season (i.e., April forecast) in Eastern Australia, when wheat producers make their most crucial management decisions. Crop model simulations were performed for commercially-grown wheat varieties, as well as for virtual genotypes representing possible combinations of phenology alleles that are currently present in the Australian wheat germplasm pool. Different adaptation strategies were tested at the site level, across Eastern Australia, for a wide range of sowing dates and nitrogen applications over long-term historical weather records (1900–2016). The results highlight that a fixed adaptation system, with genotype maturities, sowing time, and nitrogen application adapted to each location would greatly increase wheat productivity compared to sowing a mid-maturity genotype, mid-season, using current practices for nitrogen applications. Tactical adaptation of both genotype and management to the different SOI phases and to different levels of initial Plant Available Water (‘PAW & SOI adaptation’) resulted in further yield improvement. Site long-term increases in yield and gross margin were up to 1.15 t·ha−1 and AU$ 223.0 ha−1 for fixed adaptation (0.78 t·ha−1 and AU$ 153 ha−1 on average across the whole region), and up to an extra 0.26 t·ha−1 and AU$ 63.9 ha−1 for tactical adaptation. For the whole eastern region, these results correspond to an annual AU$ 440 M increase for the fixed adaptation, and an extra AU$ 188 M for the PAW & SOI tactical adaptation. The benefits of PAW & SOI tactical adaptation could be useful for growers to adjust farm management practices according to pre-sowing seasonal conditions and the seasonal climate forecast.

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“…A previsão sazonal da produtividade dos cultivos pode colaborar para a mitigação de efeitos adversos (CEGLAR et al, 2018). As informações sobre previsões climáticas podem contribuir em até 12 % na elevação dos lucros médios da produção agrícola, equivalente ao de melhoramento de plantas (RODRIGUEZ et al, 2018). Isso demonstra o quanto é importante o desenvolvimento de técnicas acuradas de previsão de safras (APARECIDO et al, 2018).…”

Section: Introductionunclassified

Modelagem Da Produtividade Do Milho Safrinha Em Função Das Condições Climáticas Do Mato Grosso Do Sul

Aparecido¹,

Torsoni²,

Mesquita³

et al. 2020

ABClima

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A previsão sazonal da produtividade dos cultivos pode colaborar para a mitigação de efeitos adversos da variabilidade climática. Portanto, objetivou-se calibrar um modelo geral de previsão de produtividade de milho safrinha para o estado do Mato Grosso do sul usando elementos climáticos. Foi utilizado séries históricas de dados climáticos como radiação solar global, temperaturas média, máxima e mínima do ar (°C), umidade relativa , velocidade do vento e precipitação pluvial durante o período de 1983-2018, e dados fitotécnicos de áreas produtivas de milho em Mato Grosso do Sul para estimar modelos e gerar mapas de produtividade no estado. A produtividade média das localidades da região norte foi maior em relação as localidades do sul do estado. O modelo desenvolvido para estimar a produtividade do milho safrinha em função das variáveis climáticas foi significativo (p < 0,05), com alta precisão (R2 aj) e acurácia (MAPE). O modelo calibrado apresentou relação significativa entre a produtividade do milho safrinha e a temperatura do ar no período de abril e maio, portanto, essa variável meteorológica pode ser utilizada como indicador de riscos climáticos em sistemas de produção de milho no estado do Mato Grosso do Sul.

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Predicting optimum crop designs using crop models and seasonal climate forecasts

Cited by 70 publications

References 39 publications

Insights into the value of seasonal climate forecasts to agriculture

Insights into the value of seasonal climate forecasts to agriculture

The Value of Tactical Adaptation to El Niño–Southern Oscillation for East Australian Wheat

Modelagem Da Produtividade Do Milho Safrinha Em Função Das Condições Climáticas Do Mato Grosso Do Sul

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