Impacts of large-scale teleconnection indices on chill accumulation for specialty crops in California.

Although the impacts of teleconnection indices on climate metrics such as precipitation and temperature in California have been widely studied, less attention has been given to the impact on integrated climate indices such as chill accumulation. This study investigates the linkages between large-scale teleconnections and winter chill accumulation for specialty crops in California, which may enable more effective and dynamic adaptation to in-season climate variability. Three large-scale teleconnection indices were selected: Oceanic Nino Index (ONI), Pacific-North American teleconnection pattern (PNA), and Pacific Decadal Oscillation (PDO) index to assess their effects on chill accumulation. The Chill Hours Model and Dynamic Model are adopted to calculate chill accumulation in Chill Hours (CH) and Chill Portions (CP) from November to January. Three major crop-producing regions, including the Central Coast, Sacramento Valley, and San Joaquin Valley, are used as the focused regions. Our results suggest CP generally has a stronger response to teleconnection patterns than CH in California. The correlations between chill accumulation and teleconnections are generally weaker during the summer than other seasons, and significant correlation can be observed 2-10 months before the start of the chill accumulation period. Among the three teleconnection indices, ONI is most weakly correlated to chill accumulation in focused regions, while PDO shows the strongest positive correlation and explains up to 39% variability of CP. PNA presents the most widespread negative correlation with chill accumulation. When aggregated to different teleconnection modes, +3.6 above-average CP is expected during ONI positive mode; +2.3 above-average CP is expected during PDO positive mode, while +2.1 above-average CP is expected during PNA negative mode. This study provides insights on early-season chill prediction and feasible management and adaptation strategies, and the methodology presented here can be used to develop decision support tools of risk control for agricultural producers and policymakers.

Impact of climate change on navel orangeworm, a major pest of tree nuts in California.

California's agricultural sector is the highest valued agricultural sector in the United States. It is also a global leader in the production of various specialty crops, including three major tree nuts - almond, walnut, and pistachio. These three nut crops accounted for approximately 16% of the state's total agricultural economy. Current and future changes in climate pose many challenges in agriculture and impacts related to increased pest pressure in agriculture due to elevated temperatures are significant. The navel orangeworm, Amyelois transitella (Walker), is the most challenging pest of tree nuts in California and often cause a significant economic loss despite the careful implementation of multiple pest control tactics. Temperature variations can directly affect the developmental rates, behavior, and overall population dynamics of this pest, and it is critically important to understand these dynamics with respect to climate change. The objective of this study was to quantify changes in the timing and number of navel orangeworm generations in almonds, walnuts, and pistachios for the entire Central Valley of California using projections from ten general circulation models (GCMs) under two emission scenarios. The results suggest that navel orangeworm is likely to complete its life cycle much faster under climate change due to projected temperature increases. The results also suggest that under future climate change, navel orangeworm can complete one additional generation within the growing season and likely going to pose significant risks to these major nut industries in the future. Quantifying navel orangeworm generations and assessing risks to tree nuts under climate change can help facilitate and strategize integrated pest management (IPM) practices to the sustainability of the production systems by minimizing risks.