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17. Spatial variation in fertilizer prices in Sub-Saharan Africa.

Author

Bonilla Cedrez, Camila, Chamberlin, Jordan, Guo, Zhe, and Hijmans, Robert J

Subjects
Crops, Agricultural, Urea, Fertilizers, Agriculture, Commerce, Africa South of the Sahara, General Science & Technology
Abstract

Low crop yields in Sub-Saharan Africa are associated with low fertilizer use. To better understand patterns of, and opportunities for, fertilizer use, location specific fertilizer price data may be relevant. We compiled local market price data for urea fertilizer, a source of inorganic nitrogen, in 1729 locations in eighteen countries in two regions (West and East Africa) from 2010-2018 to understand patterns in the spatial variation in fertilizer prices. The average national price was lowest in Ghana (0.80 USD kg-1), Kenya (0.97 USD kg-1), and Nigeria (0.99 USD kg-1). Urea was most expensive in three landlocked countries (Burundi: 1.51, Uganda: 1.49, and Burkina Faso: 1.49 USD kg-1). Our study uncovers considerable spatial variation in fertilizer prices within African countries. We show that in many countries this variation can be predicted for unsampled locations by fitting models of prices as a function of longitude, latitude, and additional predictor variables that capture aspects of market access, demand and environmental conditions. Predicted within-country urea price variation (as a fraction of the median price) was particularly high in Kenya (0.77-1.12), Nigeria (0.83-1.34), Senegal (0.73-1.40), Tanzania (0.90-1.29) and Uganda (0.93-1.30), but much lower in Burkina Faso (0.96-1.04), Burundi (0.95-1.05), and Togo (0.94-1.05). The correlation coefficient of the country level models was between 0.17 to 0.83 (mean 0.52) and the RMSE varies from 0.005 to 0.188 (mean 0.095). In 10 countries, predictions were at least 25% better than a null-model that assumes no spatial variation. Our work indicates new opportunities for incorporating spatial variation in prices into efforts to understand the profitability of agricultural technologies across rural areas in Sub-Saharan Africa.

Published
2020

18. Spatial variation in fertilizer prices in Sub-Saharan Africa

Author

Cedrez, Camila Bonilla, Chamberlin, Jordan, Guo, Zhe, and Hijmans, Robert J

Subjects
Development Studies, Agricultural, Veterinary and Food Sciences, Human Society, Clinical Research, Zero Hunger, Africa South of the Sahara, Agriculture, Commerce, Crops, Agricultural, Fertilizers, Urea, General Science & Technology
Abstract

Low crop yields in Sub-Saharan Africa are associated with low fertilizer use. To better understand patterns of, and opportunities for, fertilizer use, location specific fertilizer price data may be relevant. We compiled local market price data for urea fertilizer, a source of inorganic nitrogen, in 1729 locations in eighteen countries in two regions (West and East Africa) from 2010-2018 to understand patterns in the spatial variation in fertilizer prices. The average national price was lowest in Ghana (0.80 USD kg-1), Kenya (0.97 USD kg-1), and Nigeria (0.99 USD kg-1). Urea was most expensive in three landlocked countries (Burundi: 1.51, Uganda: 1.49, and Burkina Faso: 1.49 USD kg-1). Our study uncovers considerable spatial variation in fertilizer prices within African countries. We show that in many countries this variation can be predicted for unsampled locations by fitting models of prices as a function of longitude, latitude, and additional predictor variables that capture aspects of market access, demand and environmental conditions. Predicted within-country urea price variation (as a fraction of the median price) was particularly high in Kenya (0.77-1.12), Nigeria (0.83-1.34), Senegal (0.73-1.40), Tanzania (0.90-1.29) and Uganda (0.93-1.30), but much lower in Burkina Faso (0.96-1.04), Burundi (0.95-1.05), and Togo (0.94-1.05). The correlation coefficient of the country level models was between 0.17 to 0.83 (mean 0.52) and the RMSE varies from 0.005 to 0.188 (mean 0.095). In 10 countries, predictions were at least 25% better than a null-model that assumes no spatial variation. Our work indicates new opportunities for incorporating spatial variation in prices into efforts to understand the profitability of agricultural technologies across rural areas in Sub-Saharan Africa.

Published
2020

22. Diagnostic accuracy and performance of artificial intelligence in measuring left atrial volumes and function on multiphasic CT in patients with atrial fibrillation

Author

Aquino, Gilberto J., Chamberlin, Jordan, Yacoub, Basel, Kocher, Madison R., Kabakus, Ismail, Akkaya, Selcuk, Mercer, Megan, Waltz, Jeffrey, Fiegel, Matthew, Leaphart, Nathan, Jacob, Athira, Gulsun, Mehmet Akif, Gilkes, James, Stephenson, Joe, Sharma, Puneet, Sahbaee, Pooyan, Schoepf, Joseph, Zimmerman, Stefan, Field, Michael E., Agha, Ali M., and Burt, Jeremy R.

Published
2022
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27. Deep learning model to quantify left atrium volume on routine non-contrast chest CT and predict adverse outcomes

Author

Aquino, Gilberto J., Chamberlin, Jordan, Mercer, Megan, Kocher, Madison, Kabakus, Ismail, Akkaya, Selcuk, Fiegel, Matthew, Brady, Sean, Leaphart, Nathan, Dippre, Andrew, Giovagnoli, Vincent, Yacoub, Basel, Jacob, Athira, Gulsun, Mehmet Akif, Sahbaee, Pooyan, Sharma, Puneet, Waltz, Jeffrey, Schoepf, U. Joseph, Baruah, Dhiraj, Emrich, Tilman, Zimmerman, Stefan, Field, Michael E., Agha, Ali M., and Burt, Jeremy R.

Published
2022
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29. Automated diagnosis and prognosis of COVID-19 pneumonia from initial ER chest X-rays using deep learning

Author

Chamberlin, Jordan H., Aquino, Gilberto, Nance, Sophia, Wortham, Andrew, Leaphart, Nathan, Paladugu, Namrata, Brady, Sean, Baird, Henry, Fiegel, Matthew, Fitzpatrick, Logan, Kocher, Madison, Ghesu, Florin, Mansoor, Awais, Hoelzer, Philipp, Zimmermann, Mathis, James, W. Ennis, Dennis, D. Jameson, Houston, Brian A., Kabakus, Ismail M., Baruah, Dhiraj, Schoepf, U. Joseph, and Burt, Jeremy R.

Published
2022
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41. Quality upgrading in dairy value chains: Mixed methods evidence from southwestern Uganda

Author

Ariong, Richard M.; Chamberlin, Jordan; Kariuki, Sarah Wairimu; Van Campenhout, Bjorn and Ariong, Richard M.; Chamberlin, Jordan; Kariuki, Sarah Wairimu; Van Campenhout, Bjorn

Abstract

Innovation Policy and Scaling (IPS), Quality upgrading may be lagging in value chains where the assessment and traceability of the quality of the underlying commodity is challenging. In Uganda's southwestern milk shed, a variety of initiatives are trying to increase the quality of raw milk in dairy value chains. These initiatives generally involve the introduction of technologies that enable measurement of key quality parameters at strategic nodes in the value chain, in conjunction with a system that allows for tracking of these parameters throughout the supply chain. In this paper, we use a combination of focus group discussions, key informant interviews, and quantitative data that is generated by these initiatives to document outputs, describe emerging outcomes, and reflect on the potential impact. We find clear evidence that milk quality improved, but the effects on milk prices are more subtle.

Published
2024

42. Measuring land rental market participation in smallholder agriculture can survey design innovations improve land market participation statistics?

Author

Abate, Gashaw Tadesse; Abay, Kibrom A.; Chamberlin, Jordan; Sebsibie, Samuel and Abate, Gashaw Tadesse; Abay, Kibrom A.; Chamberlin, Jordan; Sebsibie, Samuel

Abstract

Development Strategies and Governance (DSG); Transformation Strategies; Markets, Trade, and Institutions (MTI), The emergence of rural land rental markets in Sub-Saharan Africa is recognized as a key component of the region’s ongoing economic transformation. However, the evidence base on land market participation relies on survey-derived measures, which do not always cohere when compared and triangulated, suggesting the possibility of non-trivial measurement error. We report the results of a priming and list experiments designed to shed light on a persistent mystery in rural household survey data from Africa: why there are so many fewer self-reported landlords (renters-out) than tenants (renters-in)? Our design addresses two hypotheses using experimental data from Ethiopia. First, rented-out and rented-in land may be systematically underreported because enumerators and respondents are typically primed to emphasize parcels that are actively managed/cultivated by the household. Second, rented or sharecropped-out land may be systematically underreported because of respondents’ reluctance to acknowledge an activity for which public disclosure may have negative repercussions. We address the first hypothesis with a priming experiment by exposing a random subset of respondents to a nudge that explicitly reminded them to fully account for all land, including rented/sharecropped-in and rented/sharecropped-out. We address the second hypothesis with a double-list experiment, designed to elicit true rates of land renting and sharecropping-out. We find that nudging induces about 4 percentage points increase (or 13% in relative terms) in the share of households participating in renting in or sharecropping-in practices but has negligible effects on reported rates of renting and sharecropping-out. Interestingly, our list experiment indicates much higher revealed rates of renting-out (14-15%) than is reflected in the nominal parcel-roster responses (3%). The magnitude of the latter finding fully explains the apparent difference in renting in versus renting-out rates derived from the regular p

Published
2024

43. Mapping Field-Level Maize Yields in Ethiopian Smallholder Systems Using Sentinel-2 Imagery.

Author

Mondschein, Zachary, Paliwal, Ambica, Sida, Tesfaye Shiferaw, Chamberlin, Jordan, Wang, Runzi, and Jain, Meha

Subjects
REMOTE-sensing images, SOIL weathering, CLOUDINESS, RANDOM forest algorithms, CORN farming
Abstract

Remote sensing offers a low-cost method for estimating yields at large spatio-temporal scales. Here, we examined the ability of Sentinel-2 satellite imagery to map field-level maize yields across smallholder farms in two regions in Oromia district, Ethiopia. We evaluated how effectively different indices, the MTCI, GCVI, and NDVI, and different models, linear regression and random forest regression, can be used to map field-level yields. We also examined if models improved by adding weather and soil data and how generalizable our models were if trained in one region and applied to another region, where no data were used for model calibration. We found that random forest regression models that used monthly MTCI composites led to the highest yield prediction accuracies (R2 up to 0.63), particularly when using only localized data for training the model. These models were not very generalizable, especially when applied to regions that had significant haze remaining in the imagery. We also found that adding soil and weather data did little to improve model fit. Our results highlight the ability of Sentinel-2 imagery to map field-level yields in smallholder systems, though accuracies are limited in regions with high cloud cover and haze. [ABSTRACT FROM AUTHOR]

Published
2024
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49. Optimisation of virtual monoenergetic reconstructions for the diagnosis of pulmonary embolism using photon-counting detector computed tomography angiography

Author

Chamberlin, Jordan H., primary, Toth, Adrienn, additional, Hinen, Shaun, additional, O’Doherty, Jim, additional, Baruah, Dhiraj, additional, Maisuria, Dhruw, additional, McGuire, Aaron, additional, Knight, Heather, additional, Schoepf, U. Joseph, additional, Munden, Reginald F., additional, and Kabakus, Ismail M., additional

Published
2024
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