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We aimed to describe our procedure for vascular reconstruction and back table bench preparation for the right lobe live donor allograft. Live donor liver transplantation (LDLT) remains an important option for the expansion of the donor pool. The procedure has been widely used, and its success is dependent on a technically perfect operation with appropriate inflow and outflow of the allograft. Adequate preparation of the right lobe (RL) allograft prior to implantation remains a vital part of the procedure.Our technique of back table vascular reconstruction of the RL allograft has been performed using a hepatic vein patch venoplasty, inferior hepatic vein inclusion, portal vein reconstruction, and segment V and VIII reconstruction for all of our LDLTs.Between March 2009 and January 2020, 321 consecutive adult LDLTs were performed and underwent back table reconstruction with the techniques described. During that time period, no patients had hepatic insufficiency. There was a single thrombosis of a superior mesenteric vein (SMV) to PV jump conduit.Our technique of back table reconstruction of the LDLT right lobe graft remains a crucial part of the operative procedure. Our experience with RL grafts without middle hepatic vein (MHV) and our systematic approach for inflow and outflow reconstruction has yielded excellent results with no technical outflow issues and minimal inflow complications.

This paper evaluates different methods for nowcasting country-level poverty rates, including methods that apply statistical learning to large-scale country-level data obtained from the World Development Indicators and Google Earth Engine. The methods are evaluated by withholding measured poverty rates and determining how accurately the methods predict the held-out data. A simple approach that scales the last observed welfare distribution by a fraction of real GDP per capita growth performs nearly as well as models using statistical learning on 1,000+ variables. This GDP-based approach outperforms all models that predict poverty rates directly, even when the last survey is up to five years old. The results indicate that in this context, the additional complexity introduced by applying statistical learning techniques to a large set of variables yields only marginal improvements in accuracy.

This paper evaluates the benefits of combining household surveys with satellite and other geospatial data to generate small area estimates of non-monetary poverty. Using data from Tanzania and Sri Lanka and applying a household-level empirical best (EB) predictor mixed model, we find that combining survey data with geospatial data significantly improves both the precision and accuracy of our non-monetary poverty estimates. While the EB predictor model moderately underestimates standard errors of those point estimates, coverage rates are similar to standard survey-based standard errors that assume independent outcomes across clusters.

Can features extracted from high spatial resolution satellite imagery accurately estimate poverty and economic well-being? The present study investigates this question by extracting both object and texture features from satellite images of Sri Lanka. These features are used to estimate poverty rates and average expected log consumption taken from small-area estimates derived from census data, for 1,291 administrative units. Features extracted include the number and density of buildings, the prevalence of building shadows (proxying building height), the number of cars, length of roads, type of agriculture, roof material, and several texture and spectral features. A linear regression model explains between 49 and 61 percent of the variation in average expected log consumption, and between 37 and 62 percent for poverty rates. Estimates remain accurate throughout the consumption distribution, and when extrapolating predictions into adjacent areas, although performance falls when using fewer households to calculate estimates of poverty and welfare.

This paper explores the methodological differences underlying the construction of the national consumption aggregates that are used to estimate international poverty rates for South Asian countries. The analysis draws on a regional dataset of standardized consumption aggregates to assess the sensitivity of international poverty rates to the items included in the national consumption aggregates. A key feature of the standardized aggregate is that it includes the reported value of housing rent for urban Indian homeowners. Using the standardized consumption aggregates reduces the international poverty rate in South Asia by 1.3 percentage points, impacting the status of about 18.5 million people. Comparing standardized and nonstandardized monetary welfare indicators to other nonmonetary indicators suggests that the latter are more consistent with the standardized consumption aggregates. Overall, the results strongly suggest that harmonizing the construction of welfare measures, particularly the treatment of imputed rent, can meaningfully improve the accuracy of international poverty comparisons.

Contemporary guideline-directed medical therapy (GDMT) confers a significant mortality benefit for patients with heart failure with reduced ejection fraction (HFrEF), as compared to GDMT prevalent at the time of landmark primary prevention implantable cardioverter-defibrillator (ICD) trials. The impact of modern era GDMT on survival in this population is unknown.This study sought to investigate the impact of number of GDMT medications prescribed for HFrEF on all-cause mortality in recipients of primary prevention ICD.A cohort of 4,972 recipients with primary prevention ICD (n = 3,210) or cardiac resynchronization therapy-defibrillator (CRT-D) (n = 1,762) was studied. The association of number of GDMT medications prescribed at the time of device implantation and all-cause mortality at 2 years post implantation was examined.In our primary prevention cohort, 5%, 20%, 52%, and 23% of patients were prescribed 0, 1, 2, or 3-4 GDMT medications, respectively. After risk adjustment for age, sex, ejection fraction, body mass index, the Elixhauser comorbidity score, the type of cardiomyopathy, and the year of device implantation, each additional GDMT conferred a reduction in the risk of death of 36% in recipients of ICD (HR: 0.64; P 0.001) and 30% in recipients of CRT-D (HR: 0.70; P 0.001).A higher number of prescribed GDMT medications is associated with an incremental 1-year survival in recipients of primary prevention ICD with or without CRT. Initiation of maximum number of tolerated GDMT medications should therefore be the goal for all patients with HFrEF. In the setting of robust GDMT, the risk versus benefit of a primary prevention ICD warrants re-examination in future studies.

This paper evaluates different methods for nowcasting country-level poverty rates, including methods that apply statistical learning to large-scale country-level data obtained from the World Development Indicators and Google Earth Engine. The methods are evaluated by withholding measured poverty rates and determining how accurately the methods predict the held-out data. A simple approach that scales the last observed welfare distribution by a fraction of real GDP per capita growth—a method that departs slightly from current World Bank practice—performs nearly as well as models using statistical learning on 1,000+ variables. This GDP-based approach outperforms all models that predict poverty rates directly, even when the last survey is up to five years old. The results indicate that in this context, the additional complexity introduced by applying statistical learning techniques to a large set of variables yields only marginal improvements in accuracy.

In addition to the direct health effects of the Coronavirus disease (COVID-19) pandemic, the pandemic has increased the risks of foregone non-COVID-19 healthcare. Likely, these risks are greatest in low- and middle-income countries (LMICs), where health systems are less resilient and economies more fragile. However, there are no published studies on the prevalence of foregone healthcare in LMICs during the pandemic. We used pooled data from phone surveys conducted between April and August 2020, covering 73 638 households in 39 LMICs. We estimated the prevalence of foregone care and the relative importance of various reported reasons for foregoing care, disaggregated by country income group and region. In the sample, 18.8% (95% CI 17.8–19.8%) of households reported not being able to access healthcare when needed. Financial barriers were the most-commonly self-reported reason for foregoing care, cited by 31.4% (28.6–34.3%) of households. More households in wealthier countries reported foregoing care for reasons related to COVID-19 [27.2% (22.5–31.8%) in upper-middle-income countries compared to 8.0% (4.7–11.3%) in low-income countries]; more households in poorer countries reported foregoing care due to financial reasons [65.6% (59.9–71.2%)] compared to 17.4% (13.1–21.6%) in upper-middle-income countries. A substantial proportion of households in LMICs had to forgo healthcare in the early months of the pandemic. While in richer countries this was largely due to fear of contracting COVID-19 or lockdowns, in poorer countries foregone care was due to financial constraints.

This paper investigates the impacts of the economic shock caused by the COVID-19 pandemic on the employment of different types of workers in developing countries. Employment outcomes are taken from a set of high-frequency phone surveys conducted by the World Bank and National Statistics Offices in 40 countries. Larger shares of female, young, less educated, and urban workers stopped working. Gender gaps in work stoppage were particularly pronounced and stemmed mainly from differences within sectors rather than differential employment patterns across sectors. Differences in work stoppage between urban and rural workers were markedly smaller than those across gender, age, and education groups. Preliminary results from 10 countries suggest that following the initial shock at the start of the pandemic, employment rates partially recovered between April and August, with greater gains for those groups that had borne the brunt of the early jobs losses. Although the high-frequency phone surveys greatly over-represent household heads and therefore overestimate employment rates, case studies in five countries suggest that they provide a reasonably accurate measure of disparities in employment levels by gender, education, and urban or rural location following the onset of the crisis, although they perform less well in capturing disparities between age groups. These results shed new light on the labor market consequences of the COVID-19 crisis in developing countries, and suggest that real-time phone surveys, despite their lack of representativeness, are a valuable source of information to measure differential employment impacts across groups during a crisis.

This paper investigates the relationship between countries’ regulatory environment, as measured through business, credit, labor and trade regulations, and the wage premium or penalty associated with self-employment over the period of 1970 to 2011. When considering all self-employed workers, there is no evidence that labor market regulations are associated with overall earnings penalties or premiums. However, stricter business regulations and bureaucratic red tape that restrict starting, operating, or closing a business are associated with increased earnings penalties for non-professional own-account self-employed workers, as well as premiums for employers and own-account professionals. Both more regulated trade and increased credit availability are correlated with increased earnings penalties for all self-employed workers. The association is consistently larger and more robust for female self-employed workers compared to men. These findings are consistent with regulatory restrictions on business entry and access discouraging more productive workers, particularly women, from entering non-professional own-account self-employment.

This paper examines how the design and programming of the built environment of settler cities have contributed to the invisibility of Indigenous peoples and minimizing their cultural influence. It ...

Over-expression of the Hsp70 molecular chaperone prevents protein aggregation and ameliorates neurodegenerative disease phenotypes in model systems. We identified an Hsp70 activator, MAL1-271, that reduces α-synuclein aggregation in a Parkinson’s Disease model. We now report that MAL1-271 directly increases the ATPase activity of a eukaryotic Hsp70. Next, twelve MAL1-271 derivatives were synthesized and examined in a refined α-synuclein aggregation model as well as in an assay that monitors maturation of a disease-causing Cystic Fibrosis Transmembrane Conductance Regulator (CFTR) mutant, which is also linked to Hsp70 function. Compared to the control, MAL1-271 significantly increased the number of cells lacking α-synuclein inclusions and increased the steady-state levels of the CFTR mutant. We also found that a nitrile-containing MAL1-271 analog exhibited similar effects in both assays. None of the derivatives exhibited cellular toxicity at concentrations up to 100 μm, nor were cellular stress response pathways induced. These data serve as a gateway for the continued development of a new class of Hsp70 agonists with efficacy in these and potentially other disease models.

The economic crisis caused by the COVID-19 pandemic has sharply reduced mobility and economic activity, disrupting the lives of people around the globe. This paper presents estimates on the early impact of the crisis on labor markets in 39 countries based on high-frequency phone survey data collected between April and July 2020. Workers in these countries experienced severe labor market disruptions following the COVID-19 outbreak. Based on simple averages across countries, 34 percent of the respondents reported stopping work, 20 percent of wage workers reported lack of payment for work performed, 9 percent reported job changes due to the pandemic, and 62 percent reported income loss in their household. Stopping work was more prevalent in the industrial and service sectors than in agriculture. Measures of work stoppage and income loss in the high-frequency phone survey are generally consistent with gross domestic product growth projections in Latin America and the Caribbean but not in Sub-Saharan Africa. This suggests that the survey data contribute new and important information on economic impacts in low-income countries.

This paper uses data from Sri Lanka and Tanzania to evaluate the benefits of combining household surveys with geographically comprehensive geospatial indicators to generate small area estimates of non-monetary poverty. The preferred estimates are generated by utilizing subarea-level geospatial indicators in a household-level empirical best predictor mixed model with a normalized welfare measure. Mean squared errors are estimated using a parametric bootstrap procedure. The resulting estimates are highly correlated with non-monetary poverty calculated from the full census in both countries, and the gain in precision is comparable to increasing the size of the sample by a factor of three in Sri Lanka and five in Tanzania. The empirical best predictor model moderately underestimates uncertainty, but coverage rates are similar to standard survey-based estimates that assume independent outcomes across clusters. A variety of checks, including adding noise to the welfare measure and model-based and design-based simulations, confirm that the main results are robust. The results demonstrate that combining household survey data with subarea-level geospatial indicators can greatly increase the precision of survey estimates of non-monetary poverty at comparatively low cost.

The ongoing coronavirus pandemic is expected to drastically slow 2020 GDP per capita growth in Sub-Saharan Africa (SSA) by about 5 percentage points compared to pre-pandemic forecasts. This note presents results from an analysis of a comprehensive database of surveys from 45 of 48 SSA countries to examine the effects of the project fall in growth on poverty in the region. An additional 26 million people in SSA, and as much as 58 million, may fall into extreme poverty defined by the international poverty line of 1.90 US Dollars per day in 2011 PPP. The poverty rate for SSA will likely increase more than two percentage points, setting back poverty reduction in the region by about 5 years.

This paper presents results from four simulations of the impact of potential tax reforms in Pakistan on poverty, shared prosperity, and inequality. The simulations are carried out in the context of a dynamic computational general equilibrium model that incorporates endogenous tax evasion. The simulations link the computational general equilibrium model to household survey data that are incorporated in a micro simulation model. The combined models suggest that equal yield increases in sales and corporate tax rates differ mildly in their impacts on consumption and poverty. Endogenously modeled tax evasion plays an important role in the results.

Prairie cities have large and growing Indigenous populations. The great resurgence of strong Indigenous cultures in Canada should foretell the impact Indigeneity will have over coming years across the full spectrum of urban geography. The study uses original 2014 survey data collected from seven prairie cities to examine the following question: What does the non-Indigenous majority public think about increasing the role of Indigeneity in the procedural and material approaches to urbanism in prairie cities? Based on our findings we suggest that public attitudes among the non-Indigenous populations of prairie cities are changing and becoming more positive towards trying new approaches to enabling Indigenous urbanism. Support is somewhat greater for municipal-Indigenous governance options than for specific actions to infuse Indigeneity into the design and naming of public spaces. L'attitude du public face a l'indigenisme dans l'urbanisme des Prairies canadiennes La population indigene qui habite dans les villes des Prairies est nombreuse et grandissante. La forte recrudescence des cultures indigenes au Canada permettrait d'anticiper l'impact que l'indigenisme aura au cours des prochaines annees sur l'ensemble de la geographie urbaine. Cet article propose d'explorer la question qui suit a partir des donnees originales d'enquete recueillies dans sept villes des Prairies en 2014: qu'est-ce que les personnes de la majorite non indigene pensent de l'augmentation du role de l'indigenisme dans les demarches operationnelles et substantielles de l'urbanisme des villes des Prairies? Les constats qui se degagent de nos travaux montrent que l'attitude du public chez les populations non autochtones des villes des Prairies evolue et devient plus encline a essayer de nouvelles approches en matiere d'urbanisme indigene. On est plutot partisan des modes de gouvernance municipale indigene que des actions specifiques visant a incorporer l'indigenisme dans la conception et la toponymie des espaces publics.

This paper applies an innovative method to estimate poverty in India in the absence of recent expenditure data. The method utilizes expenditure data from 2004-05, 2009–10, and 2011–12 to impute household expenditure into a survey of durable goods expenditure conducted in 2014- 15. At the 1.90 US dollar per day international poverty line, the preferred model predicts a 2014–15 head-count poverty rate of 10 percent in urban areas and 16.4 percent in rural areas, implying a poverty rate of 14.6 percent nationally. The implied poverty elasticity with respect to growth in per capita Gross Domestic Product (GDP) is within the range of past experience, and stateswith higher gross domestic product growth saw greater predicted poverty reductions. In validation tests, the model’s predictions perform comparably to the World Bank’s current adjustment method when predicting for 2011–12 but they are far more accurate when predicting for 2004–05. Three alternative specifications give moderately higher estimates of poverty. The results indicate that survey-to-survey imputation, when feasible, is a preferable alternative to the current method used to adjust survey-based poverty estimates to later years.

This paper describes a poverty reduction approach to addressing an important determinant of health and well-being among Canada’s First Nations. The Poverty Action Research Project (PARP) has its origins in the Make Poverty History Committee established by the Assembly of First Nations (AFN) in 2008. Academic members of the Committee in cooperation with the AFN subsequently applied for an action research grant to the Canadian Institutes of Health Research (CIHR). The project selected five volunteer First Nations from different parts of Canada, hiring a coordinator in each, undertaking background research, developing a profile and working with First Nation representatives in the development of a strategy to address upstream determinants of health and well-being. Subsequently, project team members within each region assisted where needed with plan implementation, supporting some initiatives with small grants. This paper provides insights from the project in several key areas, including First Nation rejection of the concept of poverty as usually defined, the importance of taking action to strengthen collectivities as well as individuals, the feasibility of assisting First Nations who are at different points in their development journey, the strengths of the leadership within the First Nations, and finding the appropriate balance between the elected and business leadership. These insights emerged from dialogue and reflection among project team members and community participants over the life of the project. We also describe what we have learned about how to engage effectively and with mutual respect with First Nations in this kind of project. The paper concludes with a review of our experiences with the policies and practices of the national research granting councils and the universities, which have not fully adjusted to the requirements of action research involving First Nations.

Country-level census data are typically collected once every 10 years. However, conflict, migration, urbanization, and natural disasters can cause rapid shifts in local population patters. This study uses Sri Lankan data to demonstrate the feasibility of a bottom-up method that combines household survey data with contemporaneous satellite imagery to track frequent changes in local population density. A Poisson regression model based on indicators derived from satellite data, selected using the least absolute shrinkage and selection operator, accurately predicts village-level population density. The model is estimated in villages sampled in the 2012/13 Household Income and Expenditure Survey to obtain out-of-sample density predictions in the non-surveyed villages. The predictions approximate the 2012 census density well are more accurate that the other bottom-up studies based on lower-resolution satellite data. The predictions are also more accurate than most publicly available population products, which rely on areal interpolation of census data to redistribute population at the local level. The accuracies are similar when estimated using a random forest model, and when density estimates are expressed in terms of population counts. The collective evidence suggests that combining surveys with satellite data is a cost-effective method to track local population changes at more frequent intervals.

This paper examines the public sector wage premium using nationally representative household surveys from 91 countries. The public sector generally pays a wage premium compared to all private sector salaried employees, but the size of the premium is sensitive to the choice of the private sector comparator and varies considerably by worker characteristics. For most countries, the average premium disappears when the public sector is compared to only formal sector private employees, especially when controlling for occupation. The public sector wage premium is higher for women and low-skilled workers. In contrast, high-skilled public sector employees are most often paid the same as their private sector counterparts or may even pay a penalty for working in the public sector. Consistent with this, the public sector premium is greater for employees with less education, those working in lower paid occupations, and those whose earnings fall in the lower part of the conditional earnings distribution. Across countries, the wage premium is only weakly associated with countries’ level of development. These findings nuance the existing consensus that public sector workers tend to enjoy a significant wage premium over their private sector counterparts, and that this premium is especially large in low-income countries.

Educating for a sustainable future and learning to live within our planetary limits is the most pressing challenge of our times. In this chapter, the authors present an emerging model of transcultural education that brings together Indigenous and western knowledges. This approach aims to engage learners from different cultures and knowledge traditions with the purpose of guiding them through ideas and processes of imagining, listening, speaking, and working together in a way that respects differences, acknowledges common ground, and seeks to co-create new knowledges. Bringing together Indigenous and Western knowledges in this manner creates a unique context that can potentially build the mindsets, skills, and dispositions that are needed for living and managing sustainably. A pedagogy grounded in this approach can potentially promote student interest and engagement across cultural and social divides, foster successful learning about bridging social inequalities, and cultivate an ethos of social, cultural, and environmental responsibility.

This paper explores the methodological differences underlying the construction of the national consumption aggregates that are used to estimate international poverty rates for all countries in the South Asia region, including Afghanistan, Bangladesh, Bhutan, India, Maldives, Nepal, Pakistan, and Sri Lanka. The analysis draws on a regional dataset of standardized consumption aggregates to assess the sensitivity of international poverty rates to the items included in national consumption aggregates. A key feature of the standardized aggregate is that it includes the reported value of housing rent for urban Indian homeowners. Using the standardized consumption aggregates reduces the international poverty rate in South Asia by 1.3 percentage points, or about 18.5 million people. Comparing standardized and non-standardized monetary welfare indicators to other nonmonetary indicators suggests that the latter are more consistent with the standardized consumption aggregates. Overall, the results strongly suggest that harmonizing the construction of welfare measures, particularly the treatment of imputed rent, can meaningfully improve the accuracy of international poverty comparisons.

Country-level census data are typically collected once every 10 years. However, conflicts, migration, urbanization, and natural disasters can rapidly shift local population patterns. This study demonstrates the feasibility of a "bottom-up"-method to estimate local population density in the between-census years by combining household surveys with contemporaneous geo-spatial data, including village-area and satellite imagery-based indicators. We apply this technique to the case of Sri Lanka using Poisson regression models based on variables selected using the Least Absolute Shrinkage and Selection Operator (LASSO). The model is estimated in villages sampled in the 2012/13 Household Income and Expenditure Survey, and is employed to obtain out-of-sample density estimates in the non-surveyed villages. These estimates approximate the census density accurately and are more precise than other bottom-up studies using similar geo-spatial data. While most open-source population products redistribute census population "top-down" from higher to lower spatial units using areal interpolation and dasymetric mapping techniques, these products become less accurate as the census itself ages. Our method circumvents the problem of the aging census by relying instead on more up-to-date household surveys. The collective evidence suggests that our method is cost effective in tracking local population density with greater frequency in the between-census years.

This paper intends to inform the effort of the Sri Lankan government to reform the targeting efficacy of its social protection programs, in particular, Samurdhi, which currently distributes benefits based on self-reported income. The paper develops a proxy means test for Sri Lanka based on the Household Income and Expenditure Survey 2016 and evaluates its performance for targeting benefits of Samurdhi. The paper considers a range of models and policy parameters that could be applied depending on data availability and country preferences. The results indicate that switching to a proxy means test could considerably improve the targeting performance of Samurdhi and would significantly improve the poverty impact of the program. The analysis finds that the performance of the proposed proxy means test model suffers when the coefficients are estimated from samples smaller than 1,000 households. However, the analysis does not find a similar loss ofmodel performance when the model is estimated from seasonal data, provided the sample size is sufficiently large. The proposed model could be applied to targeting a variety of safety net programs after validating and refining the model by conducting a pilot survey.

This paper uses household surveys from 89 countries to look at gender differences in poverty in the developing world. In the absence of individual-level poverty data, the paper looks at what can we learn in terms of gender differences by looking at the available individual and household level information. The estimates are based on the same surveys and welfare measures as official World Bank poverty estimates. The paper focuses on the relationship between age, sex and poverty. And finds that, girls and women of reproductive age are more likely to live in poor households (below the international poverty line) than boys and men. It finds that 122 women between the ages of 25 and 34 live in poor households for every 100 men of the same age group. The analysis also examines the household profiles of the poor, seeking to go beyond headship definitions. Using a demographic household composition shows that nuclear family households of two married adults and children account for 41 percent of poor households, and are the most frequent household where poor women are found. Using an economic household composition classification, households with a male earner, children and a non-income earner spouse are the most frequent among the poor at 36 percent, and the more frequent household where poor women live. For individuals, as well as for households, the presence of children increases the household likelihood to be poor, and this has a specific impact on women, but does not fully explain the observed female poverty penalty.

Although Sri Lanka has made significant progress in social and economic development over the past decade, the Northern and Eastern provinces that faced the brunt of the decades-long conflict remain disproportionately poor. To understand the labor market dimensions of poverty in these regions, this paper examines a range of job-related indicators, using data from 2011 to 2015. The overall labor force participation rate in these provinces is significantly lower than in the rest of the country. Much of the difference can be attributed to adult women, although the participation rates of youth and those with lower educational attainment are also low. The distribution of wages for male and female wage workers in these provinces is similar to that in other parts of the country. The pattern of low employment rates and comparable wages is consistent with a combination of low demand for labor and greater reluctance to work in these regions, which each depress employment but have counteracting effects on equilibrium wages. Skills are an issue, as adults in these provinces tend to score lower on literacy tests and have lower self-reported skills in reading, writing, and numeracy. Households in these provinces have less access to formal finance, which may also contribute to a lack of self-employment opportunities.

As part of the global data revolution, an increasing number of World Bank projects are based on insights from big data sources, including satellite-based measurements. Many use innovative machine- and deep-learning techniques to understand factors key to development. Leading satellite-based initiatives include: Monitoring Electrification from Space: Through analyzing 2 decades of satellite images for nightly light output from India’s 600,000 villages, this project developed a novel data-intensive strategy to improve the monitoring of rural electricity provision. The data is accessible via an online visualization platform to help optimize electrification planning. Mapping Poverty by Satellite: To generate inexpensive, timely poverty estimates, this project examined how well satellite indicators contribute to poverty prediction, and how this depends on the type of prediction model. When compared with Sri Lankan census data, high-resolution satellite indicators track poverty very well, with potential to improve traditional poverty maps. Satellite-Based Yield Measurement: Through trials in Uganda, this project is testing a pioneering approach which relates satellite-based data to plot-level ground measures of yields. This enables future yield predictions, which can inform better policymaking to help farmers improve productivity.