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Earth and Planetary Sciences » Oceanography and Atmospheric Sciences » "Climate Change - Realities, Impacts Over Ice Cap, Sea Level and Risks", book edited by Bharat Raj Singh, ISBN 978-953-51-0934-1, Published: January 16, 2013 under CC BY 3.0 license. © The Author(s).

# Potential Impacts of and Adaptation to Future Climate Change for Crop Farms: A Case Study of Flathead Valley, Montana

By Tony Prato and Zeyuan Qiu
DOI: 10.5772/39265

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## Overview

Figure 1. Location of Flathead County in Montana (a) and Flathead Valley (oval-shaped area) (b)

Figure 2. Means and one-standard deviation error bars for simulated crop enterprise net returns per ha (in 2008 dollars) for the historical period, by soil type (drpe is dry peas, swht is spring wheat, wwht is winter wheat, iral is irrigated alfalfa, dral is dry (unirrigated) alfalfa, sple is spring lentils, drca is dry (unirrigated) canola, and spbl is spring barley)

Figure 3. Means and one-standard deviation error bars for simulated crop enterprise net returns per ha (in 2008 dollars) for the future period, by soil type (crop enterprise legend given in Fig. 2)

Figure 4. Means and one-standard deviation error bars for simulated net farm income in 2008 dollars for crop production systems (CPSs) in the historical period, by soil type (crop enterprises for each CPS are listed in Table 1)

Figure 5. Means and one-standard deviation errors bars for simulated net farm income in 2008 dollars for crop production systems (CPSs) for the future period without adaptation to climate scenarios, by soil type (crop enterprises for each CPS are listed in Table 1)

# Potential Impacts of and Adaptation to Future Climate Change for Crop Farms: A Case Study of Flathead Valley, Montana

Tony Prato1 and Zeyuan Qiu2

## 1. Introduction

Greenhouse gas emissions alter carbon and hydrologic cycles, mean surface air temperature, the spatial and temporal distribution of energy, water, and nutrients, atmospheric CO2 concentration, and the frequency and severity of storms (Adams et al., 1990; National Research Council, 2001; Reilly 2002; Wang and Schimel 2003; Smith 2004; IPCC, 2007). A major consequence of increasing greenhouse gas emissions is climate change and variability (CCV). CCV alters annual levels and intra-annual patterns of temperature, precipitation, and other climate-related variables, which can impact crop yields and the profitability of crop farming. Such impacts are likely to vary across agricultural production areas. Crop yields are projected to increase in agricultural production areas experiencing slightly higher surface air temperature and growing season precipitation, and decrease in production areas experiencing significantly higher surface air temperature, lower growing season precipitation, and inadequate irrigation water supplies (McCarthy et al., 2001). Even if future CCV causes crop yields to decrease, crop farmers may be able to reduce those negative impacts by adapting their crop enterprises and crop production systems (CPSs) (i.e., combinations of crop enterprises) to actual or expected CCV (Stewart et al., 1998; Smit et al., 2000; Walther et al., 2002; Spittlehouse & Stewart, 2003; Antle et al., 2004; Easterling et al., 2004; Inkley et al., 2004). Most previous studies of CCV impacts on agriculture: (1) focus on how CCV is likely to impact regional or national crop yields; (2) do not consider CCV impacts on net farm income; and (3) do not evaluate the extent to which adapting crop enterprises and farms to CCV reduces adverse impacts of CCV. Because crop farming is a business, crop farmers need to consider the potential impacts of CCV on their financial returns; particularly impacts on crop enterprise net returns and net farm income.

## 2. Objectives

The objectives of this chapter are: (1) to assess the impacts of climate change on the levels of crop enterprise net returns and net farm income (NFI) in a future period (2006–2050) relative to their levels in an historical period (1960–2005) for small and large representative farms in Flathead Valley, Montana-the study area; and (2) to determine whether adapting CPSs to future climate change in Flathead Valley results in superior or inferior levels of net farm income compared to not adapting to future climate change. Small and large representative farms use a mix of crop enterprises, farming operations, and crop acreages, and have total sizes similar to actual small and large farms in the study area.

## 3. Previous research

Several studies have examined how climate change might affect agriculture. Reilly (2002) used the Hadley Center and Canadian climate models to estimate potential impacts of climate change on 2030-2090 crop yields for the entire US. He found that future climate change could result in: (1) higher yields for cotton, corn for grain and silage, soybeans, sorghum, barley, sugar beets, and citrus fruits; (2) higher or lower yields for wheat, rice, oats, hay, sugarcane, potatoes, and tomatoes, depending on the climate scenario; (3) large increases in average grain yields for the northern half of the Midwest, West, and Pacific Northwest; (4) depending on the climate scenario and time period, either increases or decreases in crop yields in other regions of the US; and (5) large reductions in crop yields in the South and Plains States for climate scenarios with low precipitation and substantial warming. For the Midwestern United States, Brown and Rosenberg (1997) simulated the impacts of climate change on crop yields and water use under different future climate scenarios using the Environmental/Policy Integrated Climate (EPIC) model (Williams et al., 1989). In a similar study, Izaurralde et al. (2003) used the EPIC model to evaluate the potential impacts of climate change on US crop yields, yield variability, incidence of various crop stress factors, evapotranspiration, and national crop production. That study evaluated how a baseline climate scenario for the period 1961–1990 and two Hadley Center climate scenarios for the periods 2025–2034 and 2090–2099 impact 204 representative farms. Reilly (2002), Brown and Rosenberg (1997), and Izaurralde et al. (2003) did not evaluate how future climate change is likely to impact crop enterprise net returns and NFI for representative farms as does this study. Kaiser et al. (1993) evaluated the economic and agronomic impacts of several climate warming scenarios, mainly temperature changes, on a grain farm in southern Minnesota and alternative ways to adapt the farm to those scenarios. That study did not evaluate the impacts of other climate variables, such as precipitation and atmospheric CO2 concentration, on crop yields as does this study.

Antle et al. (1999) evaluated the impacts of climate change on crop enterprise returns in the Great Plains. That study showed: (1) with adaptation of crop enterprises to climate change, climate change and CO2 enrichment caused mean crop enterprise return to change by -11% to +6% and variability in crop enterprise return to increase 7–25% relative to the baseline climate; and (2) without adaptation, mean crop enterprise return decreases 8–31% and variability in crop enterprise return increases 25–83% relative to the baseline climate. Antle et al. (2004) examined relative and absolute economic measures of the vulnerability of dryland grain farms in Montana to climate change with and without adaptation using data from a statistically representative sample of farm fields. That study allowed inferences to be drawn about the vulnerability of a heterogeneous population of farms to climate change with and without adaptation, and showed that when both climate change and higher atmospheric CO2 concentrations are taken into account, average crop enterprise return was higher relative to the baseline climate for five and lower for three of the eight adaptation scenarios evaluated. Although Antle et al. (1999, 2004) evaluated the potential impacts of climate change on crop yields and crop enterprise returns, they did not consider potential impacts of future climate change on NFI as does this study.

## 6. Results

### 6.1. Impact assessment

Simulated annual crop yields for the same crop were very similar across the three climate scenarios because IPCC climate projections of monthly temperature and precipitation are very similar across the three climate scenarios. The latter occurs because the divergence in the time paths of temperature and precipitation for the three climate scenarios does not take place until the latter half of the IPCC assessment period (i.e., 2055–2100), which occurs after the future period. Because simulated yields for a given crop are very similar across the three climate scenarios and the 100 simulated crop prices and production costs for a given crop are the same across the three scenarios, crop enterprise net returns for the same crop and soil type and NFI for the same CPS and soil type are likewise similar across the three climate scenarios. For that reason, results for the future period are averages of the results for the three climate scenarios.

#### 6.1.1. Means and standard deviations of simulated crop enterprise net returns

Means and one-standard deviation error bars for simulated crop enterprise net returns per ha are given in Fig. 2 for the historical period and Fig. 3 for the future period. Between the historical and future periods, enterprise net returns: (1) decreases by 84.3% on average for spring barley, dry canola, dry and irrigated alfalfa, oats (in Ce soil), and spring wheat (in Ce soil); and (2) increases by 44% on average for dry lentils, oats (in Ke soil), winter wheat, spring wheat (in Ke soil), and dry peas. Averaged over the nine crop enterprises and two soil types, mean simulated crop enterprise net return per ha is 24% lower in the future period than in the historical period. In summary, mean simulated net return per ha for the same crop enterprise is between 202% lower and 74% higher in the future period than in the historical period.

#### 6.1.2. Means and standard deviations of net farm income for crop production systems

The mean and one-standard deviation error bars for simulated NFIs for the six CPSs in the historical period are shown in Fig. 4 for the historical period and Fig. 5 for the future period. Simulated NFIs for CPSs in the future period assume no adaptation to climate scenarios. As expected, for both periods, simulated NFI is higher for the large representative farm (i.e., CPS 1, CPS 2, and CPS 3) than for the small representative farm (i.e., CPS 4, CPS 5, and CPS 6). In four of the six cases in the historical period, mean simulated NFI is higher for Ke soil than Ce soil. For the historical period and large representative farm, the mean simulated NFI is highest for CPS 3 in Ce soil at $87,275 and lowest for CPS 1 in Ce soil at$65,568. For the historical period and small representative farm, mean simulated NFI is highest for CPS 6 in Ke soil at $23,612 and lowest for CPS 4 in Ce soil at$21,599. For the future period and large representative farm, the mean simulated NFI is highest for CPS 3 in Ke soil for climate scenario B1 at $40,571 and lowest for CPS 3 in Ce soil for climate scenario A2 at$14,585. For the future period and small representative farm, the mean simulated NFI is highest for CPS 6 in Ke soil for climate scenario A2 at $13,726 and lowest for CPS 5 in Ce soil for climate scenario A2 at$8,864. Mean simulated NFIs for the CPSs decrease 57% between the historical and future periods. The maximum percent decline in mean simulated NFI between the historical and future periods is 83% for CPS 3 in Ce soil for the large representative farm under climate scenario A2. The minimum percent decline in mean simulated NFI between the historical and future periods is 41.9% for CPS 6 in Ke soil for the small representative farm under climate scenario A2. In summary, mean simulated net farm income for the same CPS is between 42% and 83% lower in the future period than in the historical period.

### Figure 2.

Means and one-standard deviation error bars for simulated crop enterprise net returns per ha (in 2008 dollars) for the historical period, by soil type (drpe is dry peas, swht is spring wheat, wwht is winter wheat, iral is irrigated alfalfa, dral is dry (unirrigated) alfalfa, sple is spring lentils, drca is dry (unirrigated) canola, and spbl is spring barley)

### Figure 3.

Means and one-standard deviation error bars for simulated crop enterprise net returns per ha (in 2008 dollars) for the future period, by soil type (crop enterprise legend given in Fig. 2)

### Figure 4.

Means and one-standard deviation error bars for simulated net farm income in 2008 dollars for crop production systems (CPSs) in the historical period, by soil type (crop enterprises for each CPS are listed in Table 1)

### Figure 5.

Means and one-standard deviation errors bars for simulated net farm income in 2008 dollars for crop production systems (CPSs) for the future period without adaptation to climate scenarios, by soil type (crop enterprises for each CPS are listed in Table 1)

### 6.2. Adaptation evaluation

Table 4 shows the dominant CPSs for the small and large representative farms and two soil types for the historical period and three climate scenarios. For the large representative farm: (1) CPS 2 dominates CPS 1 and CPS 3 for both soil types in the historical period and under climate scenarios A1B and A2 for the Ke soil type; (2) CPS 3 dominates CPS 1 and CPS 2 under climate scenario B1 for the Ke soil type; and (3) CPS 1 dominates CPS 2 and CPS 3 under all three climate scenarios for the Ce soil type. For the small representative farm: (1) CPS 4 dominates CPS 5 and CPS 6 in the historical period for both soil types; (2) CPS 4 dominates CPS 5 and CPS 6 under climate scenario A1B for both soil types; and (3) CPS 5 dominates CPS 4 and CPS 6 under climate scenarios A2 and B1 for both soil types. These results indicate that switching CPSs between the historical and future periods (i.e., adapting CPSs to future climate change) is optimal in eight of the twelve cases evaluated. Specifically, it is advantageous to switch: (1) from CPS 2 to CPS 3 under climate scenario B1 for the Ke soil type and from CPS 2 to CPS 1 under all three climate scenarios for the Ce soil type for the large representative farm; and (2) from CPS 4 to CPS 5 under climate scenarios A2 and B1 for both soil types for the small representative farm.

Table 5 reports the dominance relationships for CPSs for the large and small representative farms and two soil types between the historical period and three climate scenarios. Of particular interest are the dominance relationships for the cases in which the dominant CPS differs between the historical and future periods because these relationships indicate whether NFI in the historical period is superior or inferior to NFI with adaptation of CPSs to climate change. For the large representative farm: (1) CPS 2 in the historical period dominates CPS 3 under climate scenario B1 for soil type Ke; and (2) CPS 2 in the historical period dominates CPS 1 under all three climate scenarios for the Ce soil type. For the small representative farm: (1) CPS 4 in the historical period dominates CPS 5 under climate scenarios B1 and A2 for soil type Ke; and (2) CPS 5 under climate scenarios B1 and A2 dominates CPS 4 in the historical period for the Ce soil type. Combining the results in Tables 4 and 5 indicates that while adapting CPSs to future climate change reduces potential losses in NFI in eight of the 12 cases evaluated, in only three of those eight cases is NFI in the future period after adaptation to climate change superior to NFI in the historical period. Conversely, in five of those eight cases, NFI in the future period after adaptation to climate change is inferior to NFI in the historical period.

## 7. Conclusion

It is difficult to evaluate the potential adverse impacts of future climate change on agricultural production because of uncertainty regarding the nature and extent of future climate change and how such change is likely to influence crop yields, crop enterprise net returns, and NFIs for CPSs. Most previous studies of the agricultural impacts of climate change focus on how past climate change has influenced crop yields and/or crop enterprise net returns at the regional and/or national levels. The unique contribution of this study is that it developed a method for assessing the potential economic benefits (in terms of alleviating losses in NFI) of adapting CPSs to future climate change for representative farms in a local agricultural production area. This is an important contribution because farming is a business that requires farmers to understand the potential impacts of future climate change on NFI and determine whether adapting CPSs to future climate change alleviates negative impacts of those changes on NFI.

Averaged over the two representative farms and two soil types in Montana’s Flathead Valley, simulated net return per ha for the nine crop enterprises decreases 24% and mean simulated NFI for CPSs decreases 57% between the historical and future periods. Although adapting CPSs to future climate change reduces potential losses in NFI in eight of the 12 cases evaluated here, in only three of those eight cases is NFI in the future period after adaptation to climate change superior to NFI in the historical period. Therefore, for most part, adapting CPSs to future climate change alleviates but does not eliminate the negative impacts of that change on simulated NFI. The impact assessment and adaptation evaluation methods described here can be used to determine the potential impacts of future climate change on crop enterprise net returns and NFI for representative farms and evaluate the potential economic benefits of adapting crop enterprises and CPSs to future climate change in other agricultural production areas.

 Soil type Large representative farm Small representative farm Historical period Ke CPS 2 CPS 4 Ce CPS 2 CPS 4 Climate scenario A1B Ke CPS 2 CPS 4 Ce CPS 1 CPS 4 A2 Ke CPS 2 CPS 5 Ce CPS 1 CPS 5 B1 Ke CPS 3 CPS 5 Ce CPS 1 CPS 5

#### Table 4.

Dominant crop production systems (CPSs) for the historical period and each of the three climate scenarios (B1, A1B, and A2), by large and small representative farms and two soil typesa

 Soil Type Large representative farm Small representative farm Ke CPS 2 (H) Db CPS 3 (B1) CPS 4 (H) D CPS 5 (B1) CPS 2 (H) D CPS 2 (A1B) CPS 4 (H) D CPS 4 (A1B) CPS 2 (H) D CPS 2 (A2) CPS 4 (H) D CPS 5 (A2) Ce CPS 2 (H) D CPS 1 (B1) CPS 5 (B1) D CPS 4 (H) CPS 2 (H) D CPS 1 (A1B) CPS 4 (A1B) D CPS 4 (H) CPS 2 (H) D CPS 1 (A2) CPS 5 (A2) D CPS 4 (H)

#### Table 5.

Dominance relationships for crop production systems (CPSs) across the historical period (H) and three climate scenarios (B1, A1B, and A2), by large and small representative farms and two soil typesa

## Acknowledgement

The research reported here was supported in part by the National Research Initiative of the USDA Cooperative State Research, Education and Extension Service, grant number 2006-55101-17129.

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