Crop Consultants as Climate Consultants : An Extension Opportunity for Climate Change Communication

Extension personnel can augment climate change communication and efforts to decrease climate-related agricultural risks by engaging with producers\u27 trusted information sources, including crop consultants. Through a survey of inland Pacific Northwest wheat producers and in-depth interviews with area crop consultants, we examined relationships among producers, crop consultants, and climate change education and adaptation. We found that crop consultants are poised to communicate climate change information to producers, given their strong relationships with producers, practice of promoting adaptive management based on science, and ability to connect climate change to immediate on-farm practices. However, success in leveraging crop consultants to achieve widespread climate change adaptation will depend largely on Extension\u27s presenting the topic to them in accessible ways

Comparison of different stomatal conductance algorithms for ozone flux modelling

A multiplicative and a semi-mechanistic, BWB-type [Ball, J.T., Woodrow, I.E., Berry, J.A., 1987. A model predicting stomatalconductance and its contribution to the control of photosynthesis under different environmental conditions. In: Biggens, J. (Ed.), Progress in Photosynthesis Research, vol. IV. Martinus Nijhoff, Dordrecht, pp. 221–224.] algorithm for calculating stomatalconductance (gs) at the leaf level have been parameterised for two crop and two tree species to test their use in regional scale ozone deposition modelling. The algorithms were tested against measured, site-specific data for durum wheat, grapevine, beech and birch of different European provenances. A direct comparison of both algorithms showed a similar performance in predicting hourly means and daily time-courses of gs, whereas the multiplicative algorithm outperformed the BWB-type algorithm in modelling seasonal time-courses due to the inclusion of a phenology function. The re-parameterisation of the algorithms for local conditions in order to validate ozone deposition modelling on a European scale reveals the higher input requirements of the BWB-type algorithm as compared to the multiplicative algorithm because of the need of the former to model net photosynthesis (An

Regional disparities in the beneficial effects of rising CO2 concentrations on crop water productivity

Rising atmospheric CO2 concentrations ([CO2]) are expected to enhance photosynthesis and reduce crop water use1. However, there is high uncertainty about the global implications of these effects for future crop production and agricultural water requirements under climate change. Here we combine results from networks of field experiments1, 2 and global crop models3 to present a spatially explicit global perspective on crop water productivity (CWP, the ratio of crop yield to evapotranspiration) for wheat, maize, rice and soybean under elevated [CO2] and associated climate change projected for a high-end greenhouse gas emissions scenario. We find CO2 effects increase global CWP by 10[0;47]%–27[7;37]% (median[interquartile range] across the model ensemble) by the 2080s depending on crop types, with particularly large increases in arid regions (by up to 48[25;56]% for rainfed wheat). If realized in the fields, the effects of elevated [CO2] could considerably mitigate global yield losses whilst reducing agricultural consumptive water use (4–17%). We identify regional disparities driven by differences in growing conditions across agro-ecosystems that could have implications for increasing food production without compromising water security. Finally, our results demonstrate the need to expand field experiments and encourage greater consistency in modelling the effects of rising [CO2] across crop and hydrological modelling communities

Catalytic Social Entrepreneurship to Combat Desperate Poverty: A Systems Approach

Any credible agenda that seeks to eradicate global poverty must seek to correct the structural injustices and inequities that cause and perpetuate desperate endemic poverty. Such an agenda must aim not merely to aid the poor with grants, welfare and subsidies, but it must primarily seek to enhance the capabilities, skills, access and opportunities of the marginalized to participate on more equitable terms, in the dynamic process of overall economic growth. We apply a systems approach to poverty, the latter itself being a pernicious system. Eradication of global desperate poverty and its unjust structural causes can be done through two concurrent systems-thinking based strategies: (a) micro catalytic social entrepreneurship that leads to catalytic innovations that alleviate poverty, and (b) macro social catalytic political entrepreneurship that radically innovates legislation or designs macro-policy intervention systems that can effectively dismantle existing unjust structures of social injustice and inequities – the causes that perpetuate endemic global poverty. Using the theories of catalytic innovations and the bottom of the pyramid, we focus on solution (a) as being feasible, viable and doable and in the long run having the potential for eradicating global desperate poverty. We also provide two case studies where solution (b) was effectively implemented. The main proposition of the paper is that the use of both micro- and macro- catalyst can help alleviate poverty in the world.   Keywords: Micro catalyst, macro catalyst, global poverty, system approach, catalytic innovation, macro-policy intervention

Farmers\u27 Trust in Sources of Production and Climate Information and Their Use of Technology

A regionally representative survey of 900 Inland Pacific Northwest farmers showed that farmers trust other farmers and agribusiness most for production management decisions but trust university Extension most for climate change information. Additionally, in responding to questions about use of the Internet and mobile applications for making farm management decisions, many farmers indicated that they use the Internet daily but mobile applications much less regularly to access farm-related information. These results suggest that university Extension personnel have an important role to play in informing farmers about climate change and can do so effectively by using certain digital tools alongside other more traditional avenues for information delivery

Substantial carbon loss respired from a corn-soybean agroecosystem highlights the importance of careful management as we adapt to changing climate

Understanding agroecosystem carbon (C) cycle response to climate change and management is vital for maintaining their long-term C storage. We demonstrate this importance through an in-depth examination of a ten-year eddy covariance dataset from a corn-corn-soybean crop rotation grown in the Midwest United States. Ten-year average annual net ecosystem exchange (NEE) showed a net C sink of -0.39 Mg C ha-1 yr-1. However, NEE in 2014 and 2015 from the corn ecosystem was 3.58 and 2.56 Mg C ha-1 yr-1, respectively. Most C loss occurred during the growing season, when photosynthesis should dominate and C fluxes should reflect a net ecosystem gain. Partitioning NEE into gross primary productivity (GPP) and ecosystem respiration (ER) showed this C \u27burp\u27 was driven by higher ER, with a 51% (2014) and 57% (2015) increase from the ten-year average (15.84 Mg C ha-1 yr-1). GPP was also higher than average (16.24 Mg C ha-1 yr-1) by 25% (2014) and 37% (2015), but this was not enough to offset the C emitted from ER. This increased ER was likely driven by enhanced soil microbial respiration associated with ideal growing season climate, substrate availability, nutrient additions, and a potential legacy effect from drought

Long-Term Quality of Life following Treatment in Head and Neck Cancer Survivors with Feeding Tubes

Background: There is limited research on patients requiring long-term feeding tubes after head and neck cancer (HNC) treatment, despite the significant impact on post-treatment quality of life (QoL). Our study addresses this gap by assessing long-term feeding tube and long-term QoL (6 months and 1-year post-treatment). Methods: This is a retrospective study of patients diagnosed with HNC. All patients were offered FACT-HN at baseline, and 6 months to 1 year after completion of treatment. The FACT-HN (outcome variable) is a patient reported outcome measure for well-being. Higher scores indicate better quality of life. The exposure variable is the presence of a feeding tube at 6 months to 1 year after completion. Kruskal-Wallis tests were used to compare QoL at 6 months to 1 year according to the presence or absence of feeding tube. Results: There were significant differences between patients with or without feeding tube in terms of functional wellbeing (16.5 vs. 22.0; P=0.037), head and neck specific concerns (19.0 vs. 28.0; P=0.021), and total FACT-HN score (98 vs 122; P=0.035) with patients having feeding tubes demonstrating worse quality of life for all domains. Conclusion: In patients with HNC, continued presence of a feeding tube 6 months to 1 year following treatment was associated with worse functional wellbeing, head and neck related quality of life, and overall QoL. These findings demonstrate the areas of continued need for patients with longer standing feeding tubes and can help guide future support strategies for patients with swallowing dysfunction following treatment of HNC

High-throughput characterization, correlation, and mapping of leaf photosynthetic and functional traits in the soybean (Glycine max) nested association mapping population

Photosynthesis is a key target to improve crop production in many species including soybean [Glycine max (L.) Merr.]. A challenge is that phenotyping photosynthetic traits by traditional approaches is slow and destructive. There is proof-of-concept for leaf hyperspectral reflectance as a rapid method to model photosynthetic traits. However, the crucial step of demonstrating that hyperspectral approaches can be used to advance understanding of the genetic architecture of photosynthetic traits is untested. To address this challenge, we used full-range (500-2,400 nm) leaf reflectance spectroscopy to build partial least squares regression models to estimate leaf traits, including the rate-limiting processes of photosynthesis, maximum Rubisco carboxylation rate, and maximum electron transport. In total, 11 models were produced from a diverse population of soybean sampled over multiple field seasons to estimate photosynthetic parameters, chlorophyll content, leaf carbon and leaf nitrogen percentage, and specific leaf area (with R2 from 0.56 to 0.96 and root mean square error approximately \u3c10% of the range of calibration data). We explore the utility of these models by applying them to the soybean nested association mapping population, which showed variability in photosynthetic and leaf traits. Genetic mapping provided insights into the underlying genetic architecture of photosynthetic traits and potential improvement in soybean. Notably, the maximum Rubisco carboxylation rate mapped to a region of chromosome 19 containing genes encoding multiple small subunits of Rubisco. We also mapped the maximum electron transport rate to a region of chromosome 10 containing a fructose 1,6-bisphosphatase gene, encoding an important enzyme in the regeneration of ribulose 1,5-bisphosphate and the sucrose biosynthetic pathway. The estimated rate-limiting steps of photosynthesis were low or negatively correlated with yield suggesting that these traits are not influenced by the same genetic mechanisms and are not limiting yield in the soybean NAM population. Leaf carbon percentage, leaf nitrogen percentage, and specific leaf area showed strong correlations with yield and may be of interest in breeding programs as a proxy for yield. This work is among the first to use hyperspectral reflectance to model and map the genetic architecture of the rate-limiting steps of photosynthesis