A potato model intercomparison across varying climates and productivity levels

Abstract

A potato crop multimodel assessment was conducted to quantify variation among models and evaluate responses to climate change. Nine modeling groups simulated agronomic and climatic responses at low-input (Chinoli, Bolivia and Gisozi, Burundi)- and high-input (Jyndevad, Denmark and Washington, United States) management sites. Two calibration stages were explored, partial (P1), where experimental dry matter data were not provided, and full (P2). The median model ensemble response outperformed any single model in terms of replicating observed yield across all locations. Uncertainty in simulated yield decreased from 38% to 20% between P1 and P2. Model uncertainty increased with interannual variability, and predictions for all agronomic variables were significantly different from one model to another (P < 0.001). Uncertainty averaged 15% higher for low- vs. high-input sites, with larger differences observed for evapotranspiration (ET), nitrogen uptake, and water use efficiency as compared to dry matter. A minimum of five partial, or three full, calibrated models was required for an ensemble approach to keep variability below that of common field variation. Model variation was not influenced by change in carbon dioxide (C), but increased as much as 41% and 23% for yield and ET, respectively, as temperature (T) or rainfall (W) moved away from historical levels. Increases in T accounted for the highest amount of uncertainty, suggesting that methods and parameters for T sensitivity represent a considerable unknown among models. Using median model ensemble values, yield increased on average 6% per 100-ppm C, declined 4.6% per °C, and declined 2% for every 10% decrease in rainfall (for nonirrigated sites). Differences in predictions due to model representation of light utilization were significant (P < 0.01). These are the first reported results quantifying uncertainty for tuber/root crops and suggest modeling assessments of climate change impact on potato may be improved using an ensemble approach.

Key ecosystem services and ecological intensification of agriculture in the tropical high-Andean Puna as affected by land-use and climate changes

“…), barley, potato, and maize (Zea mays) yields ranging from 5 to 14% to be expected by 2030. Moreover, Fleisher et al (2016), tested different climate change scenarios for the region, using an ensemble of ten potato models. Key results can be summarized as follows: 1) warming is expected to decrease tuber yield, on average, 4% per C incremented; 2) reduction or increment in rainfall (À/+ 30% of actual) produced changes in the order of minus 4.9 or plus 2.5%, respectively; but in the best of the cases yields were way below their potential under irrigated conditions; and, 3) the fertilization effect due to higher CO 2 concentration was estimated as 10% per 100 ppm incremented.…”

Cropping Systems and Climate Change in Humid Subtropical Environments

“…Therefore, future climate change impact studies in the humid subtropical regions should quantify uncertainties through the use of crop model ensembles, to allow a broader understanding of the climate change impacts on crop production [101,102]. Multi-model ensembles have been shown to significantly improve crop model simulations when comparing with field measurements under a diversity of environments [86,[102][103][104][105][106][107].…”

Intercomparação de modelos de simulação da cana-de-açúcar e seu uso na avaliação da quebra de produtividade e dos impactos da irrigação em diferentes regiões do Brasil

“…A precisão e acurácia também melhoraram nessa abordagem, com R 2 = 0,83 e d = 0,95. O melhor resultado obtido com a abordagem multimodelos corrobora os recentes resultados encontrados para culturas anuais no mundo (PALOSUO et al, 2011;RÖTTER et al, 2012;ASSENG et al, 2013;BASSU et al, 2014;LI et al, 2015;MARTRE et al, 2015;FLEISHER et al, 2016) e no Brasil (BATTISTI; SENTELHAS, 2016), assim como os resultados obtidos para a cana-de-açúcar no Brasil (MARIN et al, 2015). Marin et al (2015) Para regiões onde a cultura da cana-de-açúcar tem se expandido, como nos estados de Goiás, As faixas de PA encontradas no presente estudo são similares às produtividades relatadas na literatura em condições experimentais sob cultivo de sequeiro nas regiões centro-leste (GUAZZELI; COSTA et al, 2007;VITTI et al, 2007;AMBROSANO et al, 2011;FERNANDES JUNIOR et al, 2013;FORTES et al, 2013;CRUSCIOL et al, 2014;CASTRO et al, 2014) e oeste/noroeste (DIAS et al, 1999;MAULE et al, 2001;BENETT et al, 2011;ZAMBROSI, 2011;FERNANDES JUNIOR et al, 2013) Figura 3.12 -Relação entre a produtividade potencial e a quebra de produtividade total da cana-de-açúcar médias para as 30 localidades brasileiras analisadas neste estudo Uma visão geral da magnitude da quebra de produtividade de colmos de cana-de-açúcar e a participação, em termos percentuais da QT, dos efeitos da deficiência hídrica e das práticas de manejo são apresentadas na Figura 3.13.…”

“…Pesquisas recentes têm demonstrado que o uso de modelos em conjunto (abordagem multimodelos) tem permitido reduzir as incertezas associadas às simulações de cada modelo individualmente. Isso foi observado nas culturas do trigo (PALOSUO et al, 2011;ASSENG et al, 2013;MARTRE et al, 2015), cevada (RÖTTER et al, 2012), milho (BASSU et al, 2014), arroz (LI et al, 2015), soja (BATTISTI; SENTELHAS, 2016) e batata (FLEISHER et al, 2016). Asseng et al (2013) avaliaram 27 modelos para a cultura do trigo cultivado em diversos locais ao redor do mundo e constataram que três modelos, adequadamente calibrados, são suficientes para reduzir as incertezas a um nível aceitável.…”

“…MO e MD são condições de manejo ótimo e deficiente, respectivamente. (Adaptado de RABBINGE, 1993;LÖVENSTEIN et al;VAN ITTERSUM;RABBINGE, 1997;LOBELL et al, 1999;VAN ITTERSUM et al, 2013;SENTELHAS et al, 2015) Há basicamente quatro abordagens para se calcular a PP e PA ( VAN ITTERSUM et al, 2013) (PALOSUO et al, 2011;ASSENG et al, 2013;MARTRE et al, 2015), cevada (RÖTTER et al, 2012), milho (BASSU et al, 2014), arroz (LI et al, 2015), soja (BATTISTI; SENTELHAS, 2016) e batata (FLEISHER et al, 2016). Para a cultura da cana-de-açúcar, Marin et al (2015) avaliaram o desempenho dos modelos DSSAT/CANEGRO e APSIM-Sugarcane para simulação da cana planta em condições experimentais em diversos locais no Brasil e constaram melhoria do valor estimado da produtividade de colmos e do índice de área foliar quando utilizados em conjunto.…”