Search

Measuring agricultural productivity is a multiscale spatiotemporal problem that requires multiscale solutions. In Vietnam, rice comprises a substantial portion of the cultivated area and is a major export crop that supplies much of the global food system. Understanding the when and where of rice productivity is vital to addressing changes to yields and food security, yet descriptive summarizations will vary depending on the spatial or temporal scale of analysis. This paper explores rice trends across Vietnam over a 19-year period, giving specific attention to modifiable spatiotemporal unit problems by evaluating productivity across multiple time periods and administrative levels. A generalizable procedure and tools are offered for visualizing multiscale time-series remote sensing data in matrix and map form, not only to elucidate the effects of modifiable spatiotemporal unit problems, but also to demonstrate how these problems serve as a useful research framework. Remote sensing indices (e.g., LAI and EVI) were evaluated against national and provincial estimates across Vietnam during multiple crop production periods using the Pearson Correlation Coefficient (PCC) to establish a relationship. To overcome challenges posed by long-term observations masking emerging phenomena, time-series matrices and multi-spatial and multi-temporal maps were produced to show when, where, and how rice productivity across Vietnam is changing. Results showed that LAI and EVI are favorable indices for measuring rice agriculture in Vietnam. At the province scale, LAI compared to nationally reported production estimates reached a Pearson's r of 0.960; 0.974 for EVI during the spring crop production period. For questions such as, "What portion of Vietnam exhibits a negative linear trend in rice production?", the answer depends on how space and time are organized. At the province scale, 25.4% of Vietnam can be observed as exhibiting a negative linear trend; however, when viewed at the district scale, this metric rises to 45.7%. This research contributes to the discussion surrounding ontological problems of how agricultural productivity is measured and conveyed. To better confront how agriculture is assessed, adopting a multiscale framework can provide a more holistic view than the conventional single spatial or temporal selection. [ABSTRACT FROM AUTHOR]

Marginal agricultural lands are defined here by suboptimal biophysical conditions and historically variable or low agricultural production. We characterize these areas using remotely sensed information to disentangle the biophysical and possible social factors driving marginality. Considering both the modifiable areal unit problem and the ecological fallacy problem, the heuristic we propose is generalizable across geographies and scales and provides information at multiple decision-making levels through a multiscalar interannual variability model. We present results from our study of Malawi, where the landscape is densely cultivated and smallholder farmers frequently occupy marginal lands, to illustrate the potential of a multiscalar analysis in a place where food insecurity alleviation is needed and where remote sensing can provide necessary information. Our framework for identifying marginal agricultural lands consists of (1) locating long-term agricultural land, (2) measuring interannual productivity of long-term farmed locations, and (3) assessing marginal biophysical land characteristics and the fundamental climate niche for the dominant crop (in this case maize). Productivity and marginality in Malawi are spatially organized, and an assessment of productivity at multiple scales highlights the importance of presenting both global and local spatiotemporal variability for managing agroecological variance. By disaggregating broad classes of historically marginal production and the underlying drivers of marginality, different intervention efforts can intelligently target areas most likely to receive maximum benefit. These methodologies can be applied by both policymakers and scholars to identify and target marginal agricultural areas for improved productivity and for the support of smallholder farmer livelihoods. [ABSTRACT FROM AUTHOR]

Successful scaling of agricultural development strategies is fundamental to increased production and yields, yet targeting efforts frequently fail to fully consider the underlying biophysical drivers of agricultural marginality, particularly at fine spatial resolutions. We present a heuristic for intelligent targeting, utilizing remotely sensed information to identify the intersection between marginal conditions for performance of a staple crop and the optimal niche for technologies that improve crop performance. Here, we explore the geographic potential of maize diversification with pigeonpea, a crop with soil productivity enhancing properties. Overall, 79 percent of agricultural land in Malawi exhibits climate conditions optimal for pigeonpea cultivation and, in total, approximately 51 percent of Malawian maize-based farming is expected to receive some benefit from pigeonpea integration, with 9 percent receiving predictable and substantial benefits. These findings illustrate the geographic scaling potential of pigeonpea in Malawi and provide direction for informed pigeonpea deployment and market development across the country. [ABSTRACT FROM AUTHOR]

Climate change, food security, and environmental sustainability are pressing issues faced by today's global population. As production demands increase and climate threatens crop productivity, agricultural research develops innovative technologies to meet these challenges. Strategies include biodiverse cropping arrangements, new crop introductions, and genetic modification of crop varieties that are resilient to climatic and environmental stressors. Geography in particular is equipped to address a critical question in this pursuit—when and where can crop system innovations be introduced? This manuscript presents a case study of the geographic scaling potential utilizing common bean, delivers an open access Google Earth Engine geovisualization application for mapping the fundamental climate niche of any crop, and discusses food security and legume biodiversity in Sub-Saharan Africa. The application is temporally agile, allowing variable growing season selections and the production of 'living maps' that are continually producible as new data become available. This is an essential communication tool for the future, as practitioners can evaluate the potential geographic range for newly-developed, experimental, and underrepresented crop varieties for facilitating sustainable and innovative agroecological solutions. [ABSTRACT FROM AUTHOR]