California is a global leader in the agricultural sector and produces more than 400 types of commodities. The state produces over a third of the country’s vegetables and two-thirds of its fruits and nuts. Despite being highly productive, current and future climate change poses many challenges to the agricultural sector. This paper provides a summary of the current state of knowledge on historical and future trends in climate and their impacts on California agriculture. We present a synthesis of climate change impacts on California agriculture in the context of: (1) historic trends and projected changes in temperature, precipitation, snowpack, heat waves, drought, and flood events; and (2) consequent impacts on crop yields, chill hours, pests and diseases, and agricultural vulnerability to climate risks. Finally, we highlight important findings and directions for future research and implementation. The detailed review presented in this paper provides sufficient evidence that the climate in California has changed significantly and is expected to continue changing in the future, and justifies the urgency and importance of enhancing the adaptive capacity of agriculture and reducing vulnerability to climate change. Since agriculture in California is very diverse and each crop responds to climate differently, climate adaptation research should be locally focused along with effective stakeholder engagement and systematic outreach efforts for effective adoption and implementation. The expected readership of this paper includes local stakeholders, researchers, state and national agencies, and international communities interested in learning about climate change and California’s agriculture.

California has a unique Mediterranean climate, well suited for irrigated agriculture. The climate is an important factor in making California a global leader in production of many high value crops, including procesing tomatoes (Lycopersicon esculentum Mill), with production of approximately 95% nation’s and 30% of world’s processing tomatoes. However, climate change poses many immediate and long-term challenges for state’s highly productive agricultural industry. In order to help growers manage risks, it is important to study locally relevant agronomic indicators that are viable and aligned with growers’ interests. Growing degree day models translate raw climate data into meaningful agricultural indicators which growers can utilize for immediate and long-term strategic decisions. Objective of this study was to analyze growing season trend in top five processing tomato-producing counties in California through the use of growing degree-days model and historical and future climate scenarios generated from the general circulation model (GCM). Based on the findings, the models indicated a significant decrease in the number of days between transplanting and maturity, with an expected harvest 2–3 weeks earlier than normal under current conditions and cultivars. Results from this study could be utilized to make strategic decisions such as variety selection, planting and harvest dates, agricultural water management, and studying trends in pests and diseases due to shifts and lengthening of tomato growing season in the tomato production areas of California.

Strawberry is a high value and labor-intensive specialty crop in California. The three major fruit production areas on the Central Coast complement each other in producing fruits almost throughout the year. Forecasting strawberry yield with some lead time can help growers plan for required and often limited human resources and aid in making strategic business decisions. The objectives of this paper were to investigate the correlation among various weather parameters related with strawberry yield at the field level and to evaluate yield forecasts using the predictive principal component regression (PPCR) and two machine-learning techniques: (a) a single layer neural network (NN) and (b) generic random forest (RF). The meteorological parameters were a combination of the sensor data measured in the strawberry field, meteorological data obtained from the nearest weather station, and calculated agroclimatic indices such as chill hours. The correlation analysis showed that all of the parameters were significantly correlated with strawberry yield and provided the potential to develop weekly yield forecast models. In general, the machine learning technique showed better skills in predicting strawberry yields when compared to the principal component regression. More specifically, the NN provided the most skills in forecasting strawberry yield. While observations of one growing season are capable of forecasting crop yield with reasonable skills, more efforts are needed to validate this approach in various fields in the region.

California is a global leader in production and supply of walnuts and almonds, and the state is the largest producer of peaches in the U.S. These crops have an important contribution to the California's agricultural economy. Damages to these crops from lepidopteran pests, mainly from Codling moth (Cydia pomonella) (family: Tortricidae), Peach twig borer (Anarsia lineatella) (family: Gelechiidae) and Oriental fruit moth (Grapholita molesta) (family: Tortricidae), are still high, despite the improvement in pest management activities. Given that temperature increase can directly impact the rate of growth and development of these pests, it is important to understand to what extent dynamics of these pests will change in future in California. The objective of this study was to quantify changes in the biofix, lifecycle length, and number of generations for these pests for the entire Central Valley of California. Using a well-established growing-degree days (GDD) model calibrated and validated using observations from orchards of California, and climate change projections from the Coupled Model Intercomparison Project phases 5 and 6 (CMIP5 and CMIP6) General Circulation Models, we found that biofix dates of these pests are expected to shift earlier by up to 28 days, and length of generations is expected to be shortened by up to 19 days, and up to 1.4 extra generations of these pests can be added by the end of the century depending on the scenario. Results from this work would enable industries to prioritize development of practices that are more effective in the long run, such as developing better cultural and biological pest solutions and insect tolerant varieties. Growers and researchers can take proactive actions to minimize future risks associated with these damaging pests. This work can be scalable to other pests and regions to understand regional dynamics of damaging agricultural pests under climate change.