Across the globe, agriculture is very sensitive to climate variability and change [1
]. Climate change affects global agricultural production where the impacts on crop yield range from –13.4% to +3.4% depending on the region, time horizon, and assumptions about crop models [4
]. Using different statistical approaches, it is estimated that approximately one-third of the variability in the crop yields can be associated with climate variability [5
]. In Europe, crop yields are expected to decrease by 45% to 81% by 2040–2070 [7
]. In India, although the short-term climate impacts are not expected to be severe, 15% and 22% decreases in rice and wheat production, respectively, are expected by 2100 due to future warming [9
]. In China, climate change is responsible for decreased crop yields, northward expansion of croplands, and an increase in pests. Using Environmental Policy Integrated Climate model for the US corn belt, corn and soybean yields are predicted to increase under low and medium carbon emission scenario and decline under the high carbon scenario, for 2080–2099 in comparison to the 20-year mean yields for 2015–2034 [10
] as much as by 31–43% and 67–79% for the lowest and worst-case warming scenarios by the end of the century [11
]. A comprehensive review of various climate models and climate scenarios shows that increasing intensity of extreme temperature and precipitation events will probably further decrease water availability and future crop yields [12
]. There is an ongoing need to link climate changes to impacts and onto farmer and rancher adaptations.
Several climate models, scenarios, and analytical methods to assess climate change effects on crop productivity presently exist [12
]. The Crop Environment Resource Synthesis (CERES)–Maize, Rice, and Wheat models were used in South Africa, Brazil, and China to quantify climate effects on crop yield [14
]. The previous studies using CERES-Wheat show conclusively that with certain adaptation measures, wheat yield can increase in the future in Nebraska USA, Central Europe, and Southern Australia under the climate change scenarios tested using CO2
concentration as an indicator [17
]. In eastern India, InfoCrop along with the ORYZA1 rice model was used to estimate increased yield due to elevated CO2
levels and temperature [20
]. Another model CropCyst, applied in southeastern Australia, predicted a decrease in wheat production by 25% under climate change and elevated CO2
]. In the Mackinaw watershed in central Illinois, USA, the Soil and Water Assessment Tool (SWAT) was used to predict the vulnerability of corn yield [22
]. In Italy, Tunisia, and Uganda, the AquaCrop model [23
] helped to quantify the variability of crop yields, identify the soil types and planting patterns possibly best suited to attain a viable yield, and identify effects of possible climate scenarios with and without adaptation measures in place for wheat, tomato and maize crop [7
]. A study with a multi-model approach with an ensemble of nine crop models identifies temperature-induced water stress as the major contributing factor in crop yield in the US [25
]. While these crop simulation models have helped evaluate the effects of climate change and variability of the overall crop production and yield, they use a top-down approach, do not rank the factors that influence production, and require extensive crop, soil, and meteorological data that is not always available in many regions. Results from simulation models can be considered as good as the setup and structure of models and the inputs available [26
]. They simulate changes in yield based on historic data and account for adaptation component differently [25
] but there is no standardized way to ensure the adaptability of results to the actual production.
Temperature, humidity, heat stress, and other climate components also impact cattle growth, health, immune systems, rumen physiology, reproduction rate, and mortality rate [27
]. The combined effects of climate-related stress impact both beef and dairy cattle by billions of dollars per year in the US alone [30
]. Climatic conditions can indirectly affect livestock production through changes in the quantity and nutrient concentration of cattle feed (both pastures and forage crops) [32
]. These effects are difficult to model due to complex interactions of many non-climatic variables, plant and land ecosystems, and management practices [32
]. Predicted future warming will increase livestock water demand by three times [37
], and limited water availability will further stress cattle.
Many other resources, such as labor, market, policy, technology access, and social, cultural, environmental, and ecological factors influence farmers’ ability to innovate and adapt to climate changes [38
]. This intermix of factors is well described by Richards musical analogy, wherein musicians (farmers) must interact with other musicians (social/environmental/ecological processes) in real-time during performing a piece (agricultural production process) to produce a coherent performance (agricultural production) [46
]. Regardless of the cause, farmers adapt to the changes to avoid yield and income losses. Understanding how farmers adapt to the changes in climate is vital in long-term planning to mitigate the effects of climate on agriculture [47
]. Researchers have put forward many propositions to explain why farmers adapt climate-smart technologies such as conservation agriculture [48
], transformational adaptation [49
], and systematic and targeted diversification of production systems [52
This study explores how ranchers in the Upper Colorado River Basin (UCRB) in Utah perceive the impacts of climate variability on hay and cattle production and how they have been adapting to the changes to maintain sustainable businesses. The study links quantitative and qualitative methods – climate extreme indices, correlation analysis, multiple linear regression, random forest regression, and rancher interviews to answer two questions:
Which precipitation, temperature, and natural streamflow variables affect hay production and cattle herd size in the region?
How have farmers adapted to climatic and non-climatic changes in the cattle and hay production process?
Answers to these questions help identify how climatic and non-climatic factors affect agricultural production, strategies ranchers used to adapt to climate and non-climatic factors, as well as strategies ranchers may adopt in the future. The next sections provide background on the Colorado River Basin in Utah, present the analysis methods, results, discussion, and conclusions.
2. Case Study: Colorado River Basin in Utah
The Colorado River serves 40 million people in 7 states of the USA and is one of the most over-allocated rivers in the world. The water availability in the basin is snowmelt driven where about 80% of the precipitation in the basin is in the form of snow. Since the last three decades of the 20th century, the snowmelt has shifted 2–3 weeks earlier [53
], which can be linked with the decreased availability of water during the growing season in the basin [54
]. Although discrepancies exist among the researchers based on methodological differences, there is a consensus that this region will face a drastic reduction in water supply in the coming decades [55
]. Udall and Overpack estimate a decrease in river flow in the entire basin by up to 20% by mid-century and 35% by the end of this century if business-as-usual warming continues [64
Agriculture in CRB contributes to about 15% of the total crop production and 13% of livestock in the US [65
]. 60% of the agricultural land is used to grow forage crops and pasture as feed for cattle [67
]. Most of the basin is arid and receives insufficient precipitation, therefore, 90% of cropland is irrigated to supplement the water requirement [67
]. In typical farm-ranch operations, calves are born in spring and are a part of the herd for a year. Ranchers raise and feed them on hay that they grow as cattle feed. Cattle are also fed on rangeland and pastures in the summer. Most rangelands are under the Bureau of Land Management or the United States Forest Service that lease the lands to the ranchers yearly. Ranchers round up cattle in the fall and feed the cattle on individual ranches through the winter. They use hay and other supplements to feed the cattle during the season. Young cattle are sold in the spring.
The river is managed by several treaties, regulations, and compacts that are collectively called The Law of the River. The Colorado River Compact (1922) designates Colorado, Wyoming, Utah, Arizona, and New Mexico as part of the Upper Basin [68
] (Figure 1
). Under the Upper Colorado River Basin Compact of 1948, Utah’s share of water apportioned to the upper basin is 23%. In the Upper Colorado River Basin, river flow has already declined by 16.4% in the last century [69
]. Recent trends of earlier-season snowmelt, decreasing snowpack, runoff shifts, and prolonged droughts can be a forerunner to a drier climate [65
]. The production agriculture–that includes farming, ranching, dairy, and other support industries, is a major economic driver in Utah and contributed $
3.5 billion to the state’s economy in 2014 alone [71
]. As agricultural production is dependent on water availability, climate impacts on agriculture are expected to be severe in the basin. To sustain agriculture in the basin, it is important to understand how climatic variability and other factors affect agricultural production in the region and how the ranchers and farmers adapt to climate-induced changes in production. While many studies have focused on the impact of climate change and variability on water resources in the Colorado River basin, little work has been done on the impacts of changes in climate on agricultural production that we address in the study. Figure 1
shows the 10 counties in the southern and eastern parts of Utah that were chosen for this case study.
When identifying the relationship between climate and agricultural production, the correlation test found that the climatic parameters and indices tested had correlation coefficient values less than 0.5. Temperature indices had a statistically significant correlation with cattle numbers whereas hay production did not correlate to the indices used in the study. Using indices as indicators of temperature and precipitation, this result implies that temperature has more impact on cattle and hay production than precipitation. This result is contrary to what was expected and what is presented in previous studies where climatic parameters (temperature and precipitation) have shown a significant relationship on the cattle and crop production [5
The results from the correlation test and linear regression show that climate indices and cattle and hay production do not have a linear relation. Random forest allows us to test the non-linear relationship between the variables by dividing the dataset into smaller sub-spaces. The results from the random forest regression show that climatic parameters are more important for hay production than cattle herd size as the frequency of occurrence of the indices in importance ranking is more for hay production than for cattle number (Figure 8
and Figure 9
). In the climate indices, the temperature-based indices appear to have more impact on the cattle and hay production in the region than precipitation-based indices (Figure 8
and Figure 9
). As the crops are irrigated and water supplied through storage, precipitation does not directly affect the crop production and cattle numbers. The results rank streamflow (water availability) high as an important factor in hay production. These results are in parallel to the results of previous studies in terms of ongoing post millennium drought; changes in temperature have a more pronounced effect on river flows (hence water availability) [64
]. It implies that temperature changes drive streamflow and by extension the crop production in the region. Streamflow is ranked much lower for cattle, which can be explained by the fact that herd size does not necessarily relate to water availability but on other driving factors as learned from the interviews. The acreage of hay does not appear to be important for cattle herd size (Figure 8
and Figure 9
). We explore this aspect in the interviews to identify other factors at play that can influence cattle production and the farmers’ decision to maintain the number of cattle in the herd every year. We hypothesized that climatic parameters influence hay and cattle production. Such an impact can be considered minor as per the quantitative analysis, as very few indices have a statistically significant correlation with cattle numbers and hay production. The quantitative analysis does not show a distinct pattern or relationship between climate and agriculture on the annual time scale. There is largely a consensus among farmers that year-to-year variability in temperature and precipitation harms the cattle and hay production. Many adaptation techniques were mentioned in the interviews that included changing irrigation practices and cropping patterns to produce enough forage for the cattle to maintain the number of cattle on the ranches, experimenting to produce hybrid species of cattle, that are resilient to hotter temperature and can use a wider variety of forage. Not all of these adaptation measures have been adopted as a response to climate, but some were adopted to increase convenience and reduce labor costs. Some farmers consider the changes in climate as normal, similar to what Liu et. al found in their study about perceptions of Nevada farmers on climate [94
]. Other prior studies also show that non-climatic factors such as lack of resources, limited market access, [45
], local market availability, market prices [40
], social factors such as social history, and the social nature of risk management [41
] can be the driving force behind the adaptation and changes in practices for farmers. Although local prices of cattle are generally driven by the global market, they have a strong impact on the local economy. In the UCRB in Utah, cattle prices can be a big factor affecting farmers’ decision to decide on the herd size and the crop to plant year by year.
The main limitation of this study was data availability. The only source of agricultural data was from NASS, which reports the data yearly. We could not identify the time of year at which the NASS surveys are made. Thus, the data might be missing for parts of the year and the available data may not account for an entire year. The data sets that we could use for all 10 counties of Utah in the study were only available for cattle numbers and alfalfa production. Thus, we could not identify how much of the change in hay production or cattle numbers was due to irrigation or other technology improvements. The unpredictability of the random forest model is very high, thus we ran the same model 5000 times to account for the variability in results. More variables that can account for the economic aspect of agricultural production can be included to bridge the gap. For larger datasets, deep-learning methods such as deep neural networks can be used in future studies to investigate the similar relationship between climate variables and agricultural production in future studies.
The impact of the commodity prices on a farmer’s decision to keep a herd size to a limit should be accounted for as it plays an important role in farm operations. This can be done using more sophisticated agronomic/economic models. Most farmers and ranchers are mindful of the climate impact on agriculture, but a few can adapt to the changes in climate to maintain the same profitability. The individual adaptive capacity of a farmer depends on many social and economic factors.