Preparing the Next Generation of Sustainability Scientists

Graduate programs emerging in universities over recent decades support the advanced study of sustainability issues in complex socio-environmental systems. Constructing the problem-scope to address these issues requires graduate students to integrate across disciplines and synthesize the social and natural dimensions of sustainability. Graduate programs that are designed to foster inter- and transdisciplinary research acknowledge the importance of training students to use integrative research approaches. However, this training is not available in all graduate programs that support integrative research, often requiring students to seek external training opportunities. We present perspectives from a group of doctoral students with diverse disciplinary backgrounds conducting integrative research in universities across the United States who participated in a 10-day, National Science Foundation-funded integrative research training workshop to learn and develop socio-environmental research skills. Following the workshop, students conducted a collaborative autoethnographic study to share pre- and postworkshop research experiences and discuss ways to increase integrative research training opportunities. Results reveal that students, regardless of disciplinary background, face common barriers conducting integrative research that include: (1) lack of exposure to epistemological frameworks and team-science skills, (2) challenges to effectively include stakeholder perspectives in his/her research, and (3) variable levels of committee support to conduct integrative research. To overcome the identified barriers and advance integrative research, students recommend how training opportunities can be embedded within existing graduate programs. Students advocate that both internal and external training opportunities are necessary to support the next generation of sustainability scientists

Identifying Dry-Season Rice-Planting Patterns in Bangladesh Using the Landsat Archive

In many countries, in situ agricultural data is not available and cost-prohibitive to obtain. While remote sensing provides a unique opportunity to map agricultural areas and management characteristics, major efforts are needed to expand our understanding of cropping patterns and the potential for remotely monitoring crop production because this could support predictions of food shortages and improve resource allocation. In this study, we demonstrate a new method to map paddy rice using Google Earth Engine (GEE) and the Landsat archive in Bangladesh during the dry (boro) season. Using GEE and Landsat, dry-season rice areas were mapped at 30 m resolution for approximately 90,000 km2 annually between 2014 and 2018. The method first reconstructs spectral vegetation indices (VIs) for individual pixels using a harmonic time series (HTS) model to minimize the effect of any sensor inconsistencies and atmospheric noise, and then combines the time series indices with a rule-based algorithm to identify characteristics of rice phenology to classify rice pixels. To our knowledge, this is the first time an annual pixel-based time series model has been applied to Landsat at the national level in a multiyear analysis of rice. Findings suggest that the harmonic-time-series-based vegetation indices (HTS-VIs) model has the potential to map rice production across fragmented landscapes and heterogeneous production practices with comparable results to other estimates, but without local management or in situ information as inputs. The HTS-VIs model identified 4.285, 4.425, 4.645, 4.117, and 4.407 million rice-producing hectares for 2014, 2015, 2016, 2017, and 2018, respectively, which correlates well with national and district estimates from official sources at an average R-squared of 0.8. Moreover, accuracy assessment with independent validation locations resulted in an overall accuracy of 91% and a kappa coefficient of 0.83 for the boro/non-boro stable rice map from 2014 to 2018. We conclude with a discussion of potential improvements and future research pathways for this approach to spatiotemporal mapping of rice in heterogeneous landscapes

Indian Acceptance of Cisgenic Rice: Are all GMOs the same?

India has more than 215 million food insecure people, many of whom are farmers. Genetically modified (GM) crops have the potential to alleviate this problem by increasing food supplies and strengthening farmer livelihoods. For this to occur, two factors are critical: (1) a change in the regulatory status of GM crops, and (2) consumer acceptance of GM foods. There are generally two classifications of GM crops based on how they are bred: cisgenically-bred, derived from sexually compatible organisms, and transgenically-bred, derived from sexually incompatible organisms. Consumers may view cisgenic foods as more natural than those produced via transgenesis, thus influencing consumer acceptance. This premise was the catalyst for our study—would Indian consumers accept cisgenically-bred rice and if so, how would they value cisgenics compared to conventionally-bred rice, GM-labeled rice, and “no fungicide” rice? In this willingness-to-pay study, respondents did not view cisgenic and GM rice differently. However, participants were willing-to-pay a premium for any aforementioned rice with a “no fungicide” attribute, which cisgenics and GM could provide. Lastly, 76% and 73% of respondents stated a willingness-to-consume GM and cisgenic foods, respectively

HYV Boro area, yield and production by Districts in Bangladesh, 2003-04 to 2014-15

These datasets contain annual area, yield and production information on High Yielding Variety (HYV) ‘boro’ rice in Bangladesh for the period 2003-2015. This variety is typically transplanted between December to mid-February and harvested between mid-April to June. Following units are used for preparing the dataset: Area (10000 hectare), Yield (Metric Ton/ Hectare) and Production (10000 Metric Ton) The original dataset was complied from Bangladesh Bureau of Statistics (BBS) yearbook of agricultural statistics and ICRISAT Village Dynamics Studies in South Asia (VDSA) survey. Any GIS software can be used to open and manipulate the spatial data (‘.shp’ format), which is created based on admin level 2 of Bangladesh GADM database of Global Administrative Areas. The corresponding ‘.rds’ file is provided for R-users

Yield reduction under climate warming varies among wheat cultivars in South Africa

Wheat yield is sensitive to temperature, but there could be substantial variation in this response across cultivars. Here the authors present data on the climatic responses of wheat cultivars in South Africa, highlighting which cultivars might be better able to maintain yield under warming

Crop Residue burning from high-resolution satellite imagery and PM2.5 dispersion: A case study of Mississippi County, Arkansas, USA

ABSTRACTCrop residue burns typically result in particulate matter (PM2.5), methane (CH4), carbon monoxide (CO), nitrous oxide (N2O), nitrogen oxides (NOx), volatile organic carbon (VOC), and black carbon emissions, which affect air quality and can pose a risk to public health. Currently, Arkansas farmers self-regulate crop burning using voluntary smoke management guidelines to reduce community impacts from smoke by ensuring burns take place in optimal conditions. The aim of this study is to identify burned cropland areas and examine human-caused fire PM2.5 emissions and dispersion during optimal burn conditions, specifically within Mississippi County, Arkansas, USA, using two separate methods. During the 2019 harvest season, high-resolution satellite data was used to manually identify burned areas and crop types. The total cumulative cropland burned area in 2019 was estimated to be 7,137 acres (29.03 km2). Burning harvested rice fields accounted for approximately 35% of the total annual PM2.5 emissions from all annual agricultural burning as reported in the 2017 U.S. EPA National Emissions Inventory, while PM2.5 emissions from burning corn fields were only 8% of the total estimated annual PM2.5 emissions. Approximately 43% of annual agricultural burning PM2.5 emissions occurred between 15 August and 23 October in Mississippi County. These high-resolution burned areas were not captured in the standard coarse resolution active fire products. Secondly, during the 2020 fall harvest season, we measured PM2.5 emissions using a Purple Air sensor and modeled smoke dispersion from a planned burn of rice fields following state-level voluntary guidelines. Additionally, the smoke transport model HYSPLIT was deployed to model this planned burn. The HYSPLIT results suggest that smoke disperses into the atmosphere from burns following the guidelines, limiting ground-level human exposure under optimal burning conditions.Implications: Fire has long been used as a cropland management tool to control weeds and invasive species and remove residues between crop rotations. Residue burns produce emissions that can present public health concerns. This study presents a novel attempt to quantify cropland burning with satellite imagery, estimate emission inventories by crop type, and simulate particulate matter dispersion from in-field burns. The results of this two-phase analysis show that our understanding and quantification of human-caused cropland burning and emissions can still be improved and integrated into management approaches as well as emission inventories