Spatial and temporal uncertainty of crop yield aggregations

Porwollik, Vera; Müller, Christoph; Elliott, Joshua; Chryssanthacopoulos, James; Iizumi, Toshichika; Ray, D. K.; Ruane, Alex C.; Arneth, Almut; Balkovič, Juraj; Ciais, Philippe; Deryng, Delphine; Folberth, Christian; Izaurralde, R. C.; Jones, Curtis D.; Khabarov, Nikolay; Lawrence, P.; Liu, Wenfeng; Pugh, Thomas A. M.; Reddy, Ashwan; Sakurai, Gen; Schmid, Erwin; Wang, Xuhui; Wit, Allard de; Wu, Xiuchen

doi:10.1016/j.eja.2016.08.006

Cited by 72 publications

(61 citation statements)

References 57 publications

(50 reference statements)

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“…An overview of GGCM model setups is provided in table S1 available at stacks.iop.org/ERL/13/064007/ mmedia; an overview of available GCM simulations, model years and ensemble members in table S2. Crop producing regions are masked using rainfed and irrigated areas from the MIRCA 2000 dataset (Portmann et al 2010) that is also used for aggregation of crop yield over actual harvested areas (Porwollik et al 2017). for four major staple crops (wheat, maize, soybean and rice from left to right, note that y-axis scaling is different).…”

Section: Methodsmentioning

confidence: 99%

Crop productivity changes in 1.5 °C and 2 °C worlds under climate sensitivity uncertainty

Schleußner

Deryng

Müller

et al. 2018

Environ. Res. Lett.

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Section: Methodsmentioning

confidence: 99%

Crop productivity changes in 1.5 °C and 2 °C worlds under climate sensitivity uncertainty

Schleußner

Deryng

Müller

et al. 2018

Environ. Res. Lett.

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“…The simulated P losses to the environment (outside crop fields) for each grid include P losses from surface runoff and leaching, and soil erosion. It also contributes to the Global Gridded Crop Model Intercomparison in the Agricultural Model Intercomparison and Improvement Project Porwollik et al, 2017). P removed by residues returned to the field is not a loss, but rather a recycling flux.…”

Section: Introductionmentioning

confidence: 99%

“…Liu et al, 2018). It also contributes to the Global Gridded Crop Model Intercomparison in the Agricultural Model Intercomparison and Improvement Project Porwollik et al, 2017). In addition to the baseline P fertilization schedule, that is, applying P before crop planting with actual P inputs based on the EarthStat data set (Mueller et al, 2012;West et al, 2014), we used the PEPIC model to determine an optimal P input rate (the optP scenario), which provides optimal P concentration in soil for crop uptake in each grid cell, keeping other factors unchanged.…”

Section: Introductionmentioning

confidence: 99%

Integrative Crop‐Soil‐Management Modeling to Assess Global Phosphorus Losses from Major Crop Cultivations

Liu

Yang

Ciais

et al. 2018

Global Biogeochemical Cycles

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Fertilization, crop uptake followed by plant harvest, runoff and erosion, and transformations of phosphorus (P) in soil are the major factors influencing the P balance of croplands. It is important to integrate plant‐soil‐management interactions into consistent modeling systems to determine the effect of P fertilization conditions on yields and to quantify P losses. Previous assessment of P losses on large scales did not consider the interactions among these factors. Here we applied a grid‐based crop model to estimate global P losses from three most produced crops: maize, rice, and wheat. The model was forced by detailed P input data sets over the period 1998–2002. According to our simulations, global P losses from the three crops reached 1.2 Tg P/year, and about 44% of it was due to soil erosion. The global total P losses were dominated by contributions from a few hot spot regions. Reducing P fertilizer in regions experiencing excessive P uses and hence losses, especially in China and India, could achieve the same yields as today and save about two thirds of global total P inputs, with the cobenefits of declining global total P losses by 41% and downstream water quality improvement. Reducing soil erosion and retaining more crop residues on croplands could further save P inputs and alleviate P losses. This study is of significance to determine the major factors influencing P balance across regions of the world and help policy makers to propose efficient strategies for tackling P‐driven environmental problems.

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“…The differences are not substantial in all cases, which further suggests that land-use weighting can be omitted. This is beneficial for model generalization as weighting is another level of uncertainty (Cohn, Vanwey, Spera, & Mustard, 2016;Porwollik et al, 2016).…”

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Global evaluation of a semiempirical model for yield anomalies and application to within‐season yield forecasting

Schauberger

Gornott

Wechsung

2017

Global Change Biology

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Quantifying the influence of weather on yield variability is decisive for agricultural management under current and future climate anomalies. We extended an existing semiempirical modeling scheme that allows for such quantification. Yield anomalies, measured as interannual differences, were modeled for maize, soybeans, and wheat in the United States and 32 other main producer countries. We used two yield data sets, one derived from reported yields and the other from a global yield data set deduced from remote sensing. We assessed the capacity of the model to forecast yields within the growing season. In the United States, our model can explain at least two-thirds (63%-81%) of observed yield anomalies. Its out-of-sample performance (34%-55%) suggests a robust yield projection capacity when applied to unknown weather. Out-of-sample performance is lower when using remote sensing-derived yield data. The share of weather-driven yield fluctuation varies spatially, and estimated coefficients agree with expectations. Globally, the explained variance in yield anomalies based on the remote sensing data set is similar to the United States (71%-84%). But the out-of-sample performance is lower (15%-42%). The performance discrepancy is likely due to shortcomings of the remote sensing yield data as it diminishes when using reported yield anomalies instead. Our model allows for robust forecasting of yields up to 2 months before harvest for several main producer countries. An additional experiment suggests moderate yield losses under mean warming, assuming no major changes in temperature extremes. We conclude that our model can detect weather influences on yield anomalies and project yields with unknown weather. It requires only monthly input data and has a low computational demand. Its within-season yield forecasting capacity provides a basis for practical applications like local adaptation planning. Our study underlines high-quality yield monitoring and statistics as critical prerequisites to guide adaptation under climate change.

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Spatial and temporal uncertainty of crop yield aggregations

Cited by 72 publications

References 57 publications

Crop productivity changes in 1.5 °C and 2 °C worlds under climate sensitivity uncertainty

Crop productivity changes in 1.5 °C and 2 °C worlds under climate sensitivity uncertainty

Integrative Crop‐Soil‐Management Modeling to Assess Global Phosphorus Losses from Major Crop Cultivations

Global evaluation of a semiempirical model for yield anomalies and application to within‐season yield forecasting

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