Case Study: Brazil Soybean

Transcription

1 Case Study: Brazil Soybean PCS products combine detailed local data and knowledge on farm systems and practices with best available weather data and models to address the diverse risk assessment needs of the insurance sector. This includes detailed historical analysis, ultra-large ensemble products, and careful consideration of key future risk-impacting trends such as climate and CO2 fertilization effects. Baseline historical product: PCS analyses historical planting and harvest data and trends to obtain accurate Planting Date, Cultivar Choice and Fertilizer estimates. In the charts below PCS examines correlations and time-series plots for the top 6 producing states in Brazil: Mato Grosso (top left), Parana (top right), Rio Grande do Sul, Goais, Mato Grosso do Sul, and Bahia. Together these 6 states account for 84% of soybean area in Brazil in recent years. Extended historical analysis: Here we drive the model with 112 years of historical data from the Princeton Geological Forcing (PGF) version 2 dataset ( ) and GSWP3 dataset ( ) to evaluate the simulated extreme event distribution over the 20 th century and compare this with the ~50 year overlapping distribution of historical yield data at country level available from the FAO ( ). These distributions overlap substantially, providing further confidence in the model outputs. Notably, the detrended historical FAO yield series is slightly more spread out (as expected) and includes more events at the low end.

2 Figure: Historical perspective without CO2 effects. Left: country-level time-series of simulated (GSWP3 using modern management, ; blue) and observed (FAO survey, ; yellow). Center: historical simulated and FAO yields as fractional deviation from trend. Right: histogram of historical simulated and FAO yield deviation from trend. Lines show smooth empirical distribution for GSWP with full 109 year period (green), GSWP over only (blue), and FAO over full period (yellow). Negative yield trend over using GSWP3 forcing is kg/decade, very similar to average trend from CESM LENS ensemble (see next section). Ultra-large present-day ensemble product: We drive the model with large ensembles of climate forcings from the CESM LENS project. The lens projects consists of several large ensembles of 1 degree resolution global simulations ( In total this gives approximately 3800 years with pre-industrial forcing, 2580 years with 20th century forcing, and 2850 years with future RCP8.5 forcing. As noted in the project plan, the present day transient run will be used here. Figure: 9 of year scenarios (nominally ). Mean slope of trend is kg/decade, corresponding to a roughly 4.7% decline in mean yields on average over the 85 year series.

3 Figure: Yield distributions (n=30*85) for the top 6 soybean producing states. Magnitude of the 1-in-200 year event is shown (as a percentage loss relative to mean yield). States in the south show significantly more sensitivity to climate than do central states. This reflects more stable climate and rainfall as well as the fact that central/western states are significantly larger and more environmentally diversified. Ultra-large future ensemble product: We drive the model with the b.e11.brcp85c5c future RCP8.5 ensemble from the CESM LENS project. In total this gives 30 scenarios from for a total of 2850 years of data with future RCP8.5 forcing. Because the Brazil soy season spans calendar years, this provides a 2820 full growing seasons. An example of 9 of these 30 scenarios is shown in the figure below, along with the quadratic trend. This sample does not include the beneficial effects of CO2 fertilization and thus shows strong negative results from long-term climate change. In order to estimate the magnitude of events at various return times, we look at yield deviation in a particular growing period relative to the quadratic trend estimation in that year. Nationally, we find that the distribution becomes more severe at all levels, with event sizes for 30 to 200 year return times increasing in severity by 15-25%. The estimated 1-in-200 year event for Brazil soybean increases from a 28.2% loss to 35.1%.

4 Figure: 9 of year scenarios (nominally ) with RCP8.5 and NO CO2 fertilization effect. Quadratic trend is shown indicating effect of long-term climate change under RCP 8.5. This implies an average linear rate of decline of kg/decade and a total loss of -28.1% over the full century. The state-level results for this scenario are summarized below for the top six soybean producing states. Mean yields decrease and extreme event magnitudes get worse for all six states. Mato Grosso still shows by far the most stable yield distribution, but extreme event magnitudes in the region increase by over 300% (from -5.9 to -18.4% loss relative to mean). Other states see relative extreme event magnitudes getting worse by 5-22%. Figure: Future relative yield distributions (n=30*94) for the top 6 soybean producing states. Magnitude of the 1-in-200 year event is shown (as a percentage loss relative to mean yield). Mean yields are reduced and the magnitude of the 1-in-200 year return time event becomes more severe in all six states. Notably, the extreme event magnitude in Mato Grosso more than triples.

5 CO2 fertilization effects. The large negative impacts of climate change are reversed with the inclusion of CO2 fertilization effects, which are expected to be strong for soybean because it uses the C3 photosynthetic pathway and is able to fix its own nitrogen (CO2 effects have been shown to be nitrogen limited, leading to reduced benefit for most crops in areas without sufficient fertilizers). Though the ultimate impacts of increased atmospheric CO2 are currently hotly debated and there is still much uncertainty (especially at very high CO2 levels above the roughly 550 ppm (which is roughly the [CO2] level around 2050 in RCP8.5) that have been studied in Free Air Carbon Enrichment (FACE) experiments), the preponderance of evidence suggests that soybean will enjoy improved photosynthesis rates, which tends to compensate to varying extents for the long-term effects of climate change. In the case of Brazil, these CO2 effects are not only strong enough to eliminate the negative effects of warming, but also lead to significant increases in mean yield as shown below. While CO2 fertilization effects are found to mitigate the impact of climate change on mean yields, the effect on extreme event magnitudes (relative to trend yield) is generally mixed and often negative (i.e. CO2 effects are significantly less positive in extreme bad years than in the median year). Figure: 9 of year scenarios (nominally ) with RCP8.5 and with (red) and without (blue) fertilization effects. Quadratic trend is shown indicating effect of long-term climate change under RCP 8.5.

6 Figure: Future relative yield distributions (n=30*94) for the top 6 soybean producing states with CO2 effects included. Magnitude of the 1-in-200 year event is shown (as a percentage loss relative to mean yield). Near-term vs. far-term. In order to evaluate the extent to which these effects are expected to grow with time over the 21 st century, we separate the ensemble into 2 equal halves and evaluate the extreme event measures. In the central Brazil states of Goias and Mato Grosso, we find evidence that extreme event magnitudes become rapidly more severe in the latter half of the 21 st century (1-in-200 year magnitude in Mato Grosso is only -7.9% in the period, increasing to -21.7%. In the southern states we generally see marginal increases in the magnitudes of severe events, but with the small sample and effect sizes, these effects are generally not statistically significant from no change. This is likely because strong mean effects in the latter half of the century have the effect of diminishing the capacity of extreme negative events to impact yield any more than they already do. Non-stationary risk in agriculture. In order to quantify the level of nonstationary risk in Brazilian soy production, we evaluate the distribution and the magnitude of extreme events from using a rolling-window of 50 years. We find that the magnitude of the 1-in-200 year return time extreme event becomes more severe at a rate of 2 percentage points every 3 decades (ranging from about a 27% loss event in the early 20 th century to an almost 37% loss event by the last half of the 21 st century). Figure: Non-stationary risk depicted through the estimated magnitude of 1-in-50 (blue), 1-in-100 (yellow) and 1-in-200 (green) year extreme-event using a rolling-window of 50 years from circa 1945 to circa 2075 (sample size = 1500 years in each window).