Climate analogs can catalyze cross-regional dialogs for US specialty crop adaptation.

Communication theory suggests that interactive dialog rather than information transmission is necessary for climate change action, especially for complex systems like agriculture. Climate analogs-locations whose current climate is similar to a target location's future climate-have garnered recent interest as transmitting more relatable information; however, they have unexplored potential in facilitating meaningful dialogs, and whether the way the analogs are developed could make a difference. We developed climate context-specific analogs based on agriculturally-relevant climate metrics for US specialty crop production, and explored their potential for facilitating dialogs on climate adaptation options. Over 80% of US specialty crop counties had acceptable US analogs for the mid-twenty-first century, especially in the West and Northeast which had greater similarities in the crops produced across target-analog pairs. Western counties generally had analogs to the south, and those in other regions had them to the west. A pilot dialog of target-analog pairs showed promise in eliciting actionable adaptation insights, indicating potential value in incorporating analog-driven dialogs more broadly in climate change communication.

Factors Affecting Population Dynamics of Helicoverpa zea (Lepidoptera: Noctuidae) in a Mixed Landscape with Bt Cotton and Peanut.

In North America, weather and host-plant abundance drive the population dynamics of the migratory pest Helicoverpa zea. The objectives of this study were to (i) estimate monthly abundance of H. zea moths in Bt cotton and peanut fields, (ii) document the effects of weather on H. zea trap catches, and (iii) determine larval hosts supporting H. zea populations from 2017 to 2019. Year-round trapping of H. zea moths was conducted in 16 commercial fields in two regions of the Florida Panhandle using delta traps. H. zea moth catches were associated with temperature, rainfall, and relative humidity. Larval hosts were determined by isotopic carbon analysis. Our results showed year-round H. zea flights in both regions across two years, with the highest and lowest moth catches occurring from July to September and November to March, respectively. There was no difference in catches between traps set on Bt cotton and peanut. In the Santa Rosa/Escambia counties, weather explained 59% of the variance in H. zea catches, with significant effects of temperature, relative humidity, and rainfall. In Jackson County, weather explained 38% of H. zea catches, with significant effects of temperature and relative humidity. Carbon isotopic data showed that feeding on C3 plants, including Bt cotton, occurred over most of the year, although feeding on C4 hosts, including Bt corn, occurred during the summer months. Hence overwintering and resident populations of H. zea in the Florida Panhandle may be continually exposed to Bt crops, increasing the risk for the evolution of resistance.

Supply chains for processed potato and tomato products in the United States will have enhanced resilience with planting adaptation strategies.

Food systems are increasingly challenged to meet growing demand for specialty crops due to the effects of climate change and increased competition for resources. Here, we apply an integrated methodology that includes climate, crop, economic and life cycle assessment models to US potato and tomato supply chains. We find that supply chains for two popular processed products in the United States, French fries and pasta sauce, will be remarkably resilient, through planting adaptation strategies that avoid higher temperatures. Land and water footprints will decline over time due to higher yields, and greenhouse gas emissions can be mitigated by waste reduction and process modification. Our integrated methodology can be applied to other crops, health-based consumer scenarios (fresh versus processed) and geographies, thereby informing decision-making throughout supply chains. Employing such methods will be essential as food systems are forced to adapt and transform to become carbon neutral due to the imperatives of climate change.

Weather-Based Predictive Modeling of Orange Rust of Sugarcane in Florida.

Epidemics of sugarcane orange rust (caused by Puccinia kuehnii) in Florida are largely influenced by prevailing weather conditions. In this study, we attempted to model the relationship between weather conditions and rust epidemics as a first step toward development of a decision aid for disease management. For this purpose, rust severity data were collected from 2014 through 2016 at the Everglades Research and Education Center, Belle Glade, Florida, by recording percentage of rust-affected area of the top visible dewlap leaf every 2 weeks from three orange rust susceptible cultivars. Hourly weather data for 10- to 40-day periods prior to each orange rust assessment were evaluated as potential predictors of rust severity under field conditions. Correlation and stepwise regression analyses resulted in the identification of nighttime (8 PM to 8 AM) accumulation of hours with average temperature 20 to 22°C as a key predictor explaining orange rust severity. The five best regression models for a 30-day period prior to disease assessment explained 65.3 to 76.2% of variation of orange rust severity. Prediction accuracy of these models was tested using a case control approach with disease observations collected in 2017 and 2018. Based on receiver operator characteristic curve analysis of these two seasons of test data, a single-variable model with the nighttime temperature predictor mentioned above gave the highest prediction accuracy of disease severity. These models have potential for use in quantitative risk assessment of sugarcane rust epidemics.

Greenhouse gas emissions and carbon sequestration by agroforestry systems in southeastern Brazil.

Agrosilvopastoral and silvopastoral systems can increase carbon sequestration, offset greenhouse gas (GHG) emissions and reduce the carbon footprint generated by animal production. The objective of this study was to estimate GHG emissions, the tree and grass aboveground biomass production and carbon storage in different agrosilvopastoral and silvopastoral systems in southeastern Brazil. The number of trees required to offset these emissions were also estimated. The GHG emissions were calculated based on pre-farm (e.g. agrochemical production, storage, and transportation), and on-farm activities (e.g. fertilization and machinery operation). Aboveground tree grass biomass and carbon storage in all systems was estimated with allometric equations. GHG emissions from the agroforestry systems ranged from 2.81 to 7.98 t CO2e ha-1. Carbon storage in the aboveground trees and grass biomass were 54.6, 11.4, 25.7 and 5.9 t C ha-1, and 3.3, 3.6, 3.8 and 3.3 t C ha-1 for systems 1, 2, 3 and 4, respectively. The number of trees necessary to offset the emissions ranged from 17 to 44 trees ha-1, which was lower than the total planted in the systems. Agroforestry systems sequester CO2 from the atmosphere and can help the GHG emission-reduction policy of the Brazilian government.

Evaluation of leaf wetness duration models for operational use in strawberry disease-warning systems in four US states.

Leaf wetness duration (LWD) plays a key role in disease development and is often used as an input in disease-warning systems. LWD is often estimated using mathematical models, since measurement by sensors is rarely available and/or reliable. A strawberry disease-warning system called "Strawberry Advisory System" (SAS) is used by growers in Florida, USA, in deciding when to spray their strawberry fields to control anthracnose and Botrytis fruit rot. Currently, SAS is implemented at six locations, where reliable LWD sensors are deployed. A robust LWD model would facilitate SAS expansion from Florida to other regions where reliable LW sensors are not available. The objective of this study was to evaluate the use of mathematical models to estimate LWD and time of spray recommendations in comparison to on site LWD measurements. Specific objectives were to (i) compare model estimated and observed LWD and resulting differences in timing and number of fungicide spray recommendations, (ii) evaluate the effects of weather station sensors precision on LWD models performance, and (iii) compare LWD models performance across four states in the USA. The LWD models evaluated were the classification and regression tree (CART), dew point depression (DPD), number of hours with relative humidity equal or greater than 90 % (NHRH ≥90 %), and Penman-Monteith (P-M). P-M model was expected to have the lowest errors, since it is a physically based and thus portable model. Indeed, the P-M model estimated LWD most accurately (MAE <2 h) at a weather station with high precision sensors but was the least accurate when lower precision sensors of relative humidity and estimated net radiation (based on solar radiation and temperature) were used (MAE = 3.7 h). The CART model was the most robust for estimating LWD and for advising growers on fungicide-spray timing for anthracnose and Botrytis fruit rot control and is therefore the model we recommend for expanding the strawberry disease warning beyond Florida, to other locations where weather stations may be deployed with lower precision sensors, and net radiation observations are not available.

Asian soybean rust: modeling the impact on soybean grain yield in the Triângulo Mineiro/Alto Paranaíba region, Minas Gerais, Brazil / Ferrugem asiática da soja: modelagem do impacto na produtividade da soja na região do Triângulo Mineiro/Alto Paranaíba, Minas Gerais, Brasil

Understanding the impact of Asian soybean rust on soybean yield is of great importance in the crop simulation model for this crop become it is possible to predict yield using different sowing dates and growth conditions. The goal of this study were to evaluate the performance of two soybean cultivars in Triângulo Mineiro/Alto Paranaíba, MG, Brazil and the effects of soybean rust on the yield of these cultivars using the CSM-CROPGRO Soybean model. Two soybean cultivars "NK 7074" (early) and "UFUS-Impacta" (medium late), which differ in their development cycles, were growing in Uberaba city during the 2009/2010 growing season. The validation for cultivar "UFUS-Impacta" was conducted comparing the measured and simulated yield data considering three different sowing dates in the "Uberlândia" city during the 2002/2003 growing season. Daily meteorological data obtained from six meteorological stations of the National Institute of Meteorology (INMET). To determine the performance of the soybean cultivars and the effect of soybean rust on yield, three different scenarios were used: no occurrence of rust (NOR) and occurrence of rust with inoculum concentrations of U5.000 and U10.000 urediniospores/mL. For all environments studied, the early cultivar had the best performance than the medium late cultivar. Soybean rust had the most effect on yield for the U10.000 scenario than for the U5.000 scenario. The best soybean performance occurred for "Araxá" and "Uberaba" cities. The SouthSoutheast area of the "Triângulo Mineiro/Alto Paranaíba" region was the most sensitive to the effect of rust on yield compared to the North region.

Compreender o impacto da ferrugem asiática na produtividade da soja é de grande importância para os modelos de simulação dessa cultura, pois pode-se prever a produtividade utilizando-se diferentes datas de semeadura e condições de crescimento. O objetivo deste estudo foi analisar a performance de duas cultivares de soja na região do Triângulo Mineiro/Alto Paranaíba, MG, Brasil e os efeitos da ferrugem asiática da soja na produtividade desses cultivares utilizando o modelo CSM-CROPGRO Soybean. Duas cultivares de soja NK 7074 (precoce) e UFUS-Impacta (semitardia), as quais diferem nos seus ciclos de desenvolvimento, foram cultivadas em Uberaba na safra 2009/2010. A validação para a cultivar UFUS-Impacta foi conduzida comparando os dados observados e simulados de produtividade considerando três diferentes datas de semeadura na safra 2002/2003 em Uberlândia, MG. Foram utilizados dados meteorológicos diários de seis estações meteorológicas do Instituto Nacional de Meteorologia (INMET). Para determinar o desempenho das cultivares de soja e o efeito da ferrugem na produtividade, utilizou-se três diferentes cenários denominados de: não ocorrência de ferrugem (NOR) e ocorrência de ferrugem nas concentrações de inóculo de U5.000 e U10.000 urediniósporos/mL. Para todos os ambientes estudados, a cultivar precoce teve o melhor desempenho em relação a cultivar semi-tardia. A ferrugem da soja teve maior impacto na produção para o cenário U10.000 do que para o cenário U5.000. O melhor desempenho das cultivares de soja foram para as cidades de Araxá e Uberaba. A área Sul-Sudeste do Triângulo Mineiro/Alto Paranaíba foi a mais sensível ao efeito da ferrugem na produtividade em comparação com a região norte.