Smallholder Participation in Agricultural Value Chains: Comparative Evidence from Three Continents

Supermarkets, specialized wholesalers, and processors and agro-exporters’ agricultural value chains have begun to transform the marketing channels into which smallholder farmers sell produce in low-income economies. We develop a conceptual framework through which to study contracting between smallholders and a commodity-processing firm. We then conduct an empirical meta-analysis of agricultural value chains in five countries across three continents (Ghana, India, Madagascar, Mozambique, and Nicaragua). We document patterns of participation, the welfare gains associated with participation, reasons for non-participation, the significant extent of contract non-compliance, and the considerable dynamism of these value chains, as farmers and firms enter and exit frequently.

Smallholder Participation in Agricultural Value Chains: Comparative Evidence from Three Continents

Supermarkets, specialized wholesalers, and processors and agro-exporters’ agricultural value chains have begun to transform the marketing channels into which smallholder farmers sell produce in low-income economies. We develop a conceptual framework through which to study contracting between smallholders and a commodity-processing firm. We then conduct an empirical meta-analysis of agricultural value chains in five countries across three continents (Ghana, India, Madagascar, Mozambique, and Nicaragua). We document patterns of participation, the welfare gains associated with participation, reasons for non-participation, the significant extent of contract non-compliance, and the considerable dynamism of these value chains, as farmers and firms enter and exit frequently.Agricultural Value Chains, Contract Farming, Africa, Asia, Latin America

Bone Material Analogues for PET/MRI Phantoms

Purpose: To develop bone material analogues that can be used in construction of phantoms for simultaneous PET/MRI systems. Methods: Plaster was used as the basis for the bone material analogues tested in this study. It was mixed with varying concentrations of an iodinated CT contrast, a gadolinium-based MR contrast agent, and copper sulfate to modulate the attenuation properties and MRI properties (T1 and T2*). Attenuation was measured with CT and 68Ge transmission scans, and MRI properties were measured with quantitative ultrashort echo time pulse sequences. A proof-of-concept skull was created by plaster casting. Results: Undoped plaster has a 511 keV attenuation coefficient (~0.14 cm-1) similar to cortical bone (0.10-0.15 cm-1), but slightly longer T1 (~500 ms) and T2* (~1.2 ms) MR parameters compared to bone (T1 ~ 300 ms, T2* ~ 0.4 ms). Doping with the iodinated agent resulted in increased attenuation with minimal perturbation to the MR parameters. Doping with a gadolinium chelate greatly reduced T1 and T2*, resulting in extremely short T1 values when the target T2* values were reached, while the attenuation coefficient was unchanged. Doping with copper sulfate was more selective for T2* shortening and achieved comparable T1 and T2* values to bone (after 1 week of drying), while the attenuation coefficient was unchanged. Conclusions: Plaster doped with copper sulfate is a promising bone material analogue for a PET/MRI phantom, mimicking the MR properties (T1 and T2*) and 511 keV attenuation coefficient of human cortical bone

Mixed Supervision of Histopathology Improves Prostate Cancer Classification from MRI

Non-invasive prostate cancer detection from MRI has the potential to revolutionize patient care by providing early detection of clinically-significant disease (ISUP grade group >= 2), but has thus far shown limited positive predictive value. To address this, we present an MRI-based deep learning method for predicting clinically significant prostate cancer applicable to a patient population with subsequent ground truth biopsy results ranging from benign pathology to ISUP grade group~5. Specifically, we demonstrate that mixed supervision via diverse histopathological ground truth improves classification performance despite the cost of reduced concordance with image-based segmentation. That is, where prior approaches have utilized pathology results as ground truth derived from targeted biopsies and whole-mount prostatectomy to strongly supervise the localization of clinically significant cancer, our approach also utilizes weak supervision signals extracted from nontargeted systematic biopsies with regional localization to improve overall performance. Our key innovation is performing regression by distribution rather than simply by value, enabling use of additional pathology findings traditionally ignored by deep learning strategies. We evaluated our model on a dataset of 973 (testing n=160) multi-parametric prostate MRI exams collected at UCSF from 2015-2018 followed by MRI/ultrasound fusion (targeted) biopsy and systematic (nontargeted) biopsy of the prostate gland, demonstrating that deep networks trained with mixed supervision of histopathology can significantly exceed the performance of the Prostate Imaging-Reporting and Data System (PI-RADS) clinical standard for prostate MRI interpretation

Smallholder Participation in Agricultural Value Chains: Comparative Evidence from Three Continents

Supermarkets, specialized wholesalers, and processors and agro-exporters’ agricultural value chains have begun to transform the marketing channels into which smallholder farmers sell produce in low-income economies. We develop a conceptual framework through which to study contracting between smallholders and a commodity-processing firm. We then conduct an empirical meta-analysis of agricultural value chains in five countries across three continents (Ghana, India, Madagascar, Mozambique, and Nicaragua). We document patterns of participation, the welfare gains associated with participation, reasons for non-participation, the significant extent of contract non-compliance, and the considerable dynamism of these value chains, as farmers and firms enter and exit frequently

Old Tools, New Ways of Using Them: Harnessing Expert Opinions to Plan for Surprise in Marine Socio-Ecological Systems

Copyright © 2019 Gladstone-Gallagher, Hope, Bulmer, Clark, Stephenson, Mangan, Rullens, Siwicka, Thomas, Pilditch, Savage and Thrush. With globally accelerating rates of environmental disturbance, coastal marine ecosystems are increasingly prone to non-linear regime shifts that result in a loss of ecosystem function and services. A lack of early-detection methods, and an over reliance on limits-based approaches means that these tipping points manifest as surprises. Consequently, marine ecosystems are notoriously difficult to manage, and scientists, managers, and policy makers are paralyzed in a spiral of ecosystem degradation. This paralysis is caused by the inherent need to quantify the risk and uncertainty that surrounds every decision. While progress toward forecasting tipping points is ongoing and important, an interim approach is desperately needed to enable scientists to make recommendations that are credible and defensible in the face of deep uncertainty. We discuss how current tools for developing risk assessments and scenario planning, coupled with expert opinions, can be adapted to bridge gaps in quantitative data, enabling scientists and managers to prepare for many plausible futures. We argue that these tools are currently underutilized in a marine cumulative effects context but offer a way to inform decisions in the interim while predictive models and early warning signals remain imperfect. This approach will require redefining the way we think about managing for ecological surprise to include actions that not only limit drivers of tipping points but increase socio-ecological resilience to yield satisfactory outcomes under multiple possible futures that are inherently uncertain