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Objectives: To assess the range of quantitative iodine values in renal cysts (RC) (with a few renal neoplasms [RNs] as a comparison) to develop an expected range of values for RC that can be used in future studies for their differentiation. Material and Methods: Consecutive patients (n = 140) with renal lesions who had undergone abdominal examination on a clinical photon-counting computed tomography (PCCT) were retrospectively included. Automated iodine quantification maps were reconstructed, and region of interest (ROI) measurements of iodine concentration (IC) (mg/cm3) were performed on whole renal lesions. In addition, for heterogeneous lesions, a secondary ROI was placed on the area most suspicious for malignancy. The discriminatory values of minimum, maximum, mean, and standard deviation for IC were compared using simple logistic regression and receiver operating characteristic curves (area under the curve [AUC]). Results: A total of 259 renal lesions (243 RC and 16 RN) were analyzed. There were significant differences between RC and RN for all IC measures with the best-performing metrics being mean and maximum IC of the entire lesion ROI (AUC 0.912 and 0.917, respectively) but also mean and minimum IC of the most suspicious area in heterogeneous lesions (AUC 0.983 and 0.992, respectively). Most RC fell within a range of low measured iodine values although a few had higher values. Conclusion: Automated iodine quantification maps reconstructed from clinical PCCT have a high diagnostic ability to differentiate RCs and neoplasms. The data from this pilot study can be used to help establish quantitative values for clinical differentiation of renal lesions. [ABSTRACT FROM AUTHOR]

Purpose: Acute chest syndrome (ACS) is secondary to occlusion of the pulmonary vasculature and a potentially life-threatening complication of sickle cell disease (SCD). Dual-energy CT (DECT) iodine perfusion map reconstructions can provide a method to visualize and quantify the extent of pulmonary microthrombi. Methods: A total of 102 patients with sickle cell disease who underwent DECT CTPA with perfusion were retrospectively identified. The presence or absence of airspace opacities, segmental perfusion defects, and acute or chronic pulmonary emboli was noted. The number of segmental perfusion defects between patients with and without acute chest syndrome was compared. Sub-analyses were performed to investigate robustness. Results: Of the 102 patients, 68 were clinically determined to not have ACS and 34 were determined to have ACS by clinical criteria. Of the patients with ACS, 82.4% were found to have perfusion defects with a median of 2 perfusion defects per patient. The presence of any or new perfusion defects was significantly associated with the diagnosis of ACS (P = 0.005 and < 0.001, respectively). Excluding patients with pulmonary embolism, 79% of patients with ACS had old or new perfusion defects, and the specificity for new perfusion defects was 87%, higher than consolidation/ground glass opacities (80%). Conclusion: DECT iodine map has the capability to depict microthrombi as perfusion defects. The presence of segmental perfusion defects on dual-energy CT maps was found to be associated with ACS with potential for improved specificity and reclassification. [ABSTRACT FROM AUTHOR]

Background: Detecting occlusions of coronary artery bypass grafts using non-contrast computed tomography (CT) series is understudied and underestimated. Purpose: To evaluate morphological findings for the diagnosis of chronic coronary artery bypass graft occlusion on non-contrast CT and investigate performance statistics for potential use cases. Material and Methods: Seventy-three patients with coronary artery bypass grafts who had CT angiography of the chest (non-contrast and arterial phases) were retrospectively included. Two readers applied pre-set morphologic findings to assess the patency of a bypass graft on non-contrast series. These findings included vessel shape (linear-band like), collapsed lumen and surgical graft marker without a visible vessel. Performance was tested using the simultaneously acquired arterial phase series as the ground truth. Results: The per-patient diagnostic accuracy for occlusion was 0.890 (95% confidence interval = 0.795–0.951). Venous grafts overall had an 88% accuracy. None of the left internal mammary artery to left anterior descending artery arterial graft occlusions were detected. The negative likelihood ratio for an occluded graft that is truly patent was 0.121, demonstrating a true post-test probability of 97% for identifying a patent graft as truly patent given a prevalence of 20% occlusion at a median 8.4 years post-surgery. Neither years post-surgery, nor number of vessels was associated with a significant decrease in reader accuracy. Conclusion: Evaluation of coronary bypass grafts for chronic occlusion on non-contrast CT based off vessel morphology is feasible and accurate for venous grafts. Potential use cases include low-intermediate risk patients with chest pain or shortness of breath for whom non-contrast CT was ordered, or administration of iodine-based contrast is contraindicated. [ABSTRACT FROM AUTHOR]

Purpose: Left atrial calcification (LAC), a primarily radiologic diagnosis, has been associated with rheumatic heart disease (RHD) and rheumatic fever (RF). However, left atrial calcification continues to be observed despite a significant decrease in the prevalence of rheumatic heart disease. The purpose of this study was to investigate other possible etiologies of left atrial calcification. Material and methods: This retrospective, observational single-center study included patients from 2017 to 2022 identified as having left atrial calcification as well as age- and sex-matched controls. The prevalence of rheumatic heart disease, atrial ablation, and mitral valve disease was compared, and odds ratios were calculated for each independent variable. Results: Sixty-two patients with left atrial calcifications were included and compared with 62 controls. 87.1% of patients in the left atrial calcifications cohort had a history of atrial fibrillation compared with 21% in the control cohort (p < 0.001). 16.1% of patients in the calcifications cohort presented a history of rheumatic fever compared with zero in the control cohort (p = 0.004). 66.1% of the left atrial calcifications cohort had a history of atrial ablation compared with 6.5% of the control group (p < 0.001). The odds ratio for left atrial calcification was 19.0 vs. 4.8 for rheumatic fever (comparative odds = 4.0 for atrial ablation vs. rheumatic fever). Multivariable log model found atrial ablation to explain 79.8% of left atrial calcifications identified. Conclusions: Our study found a 4-fold higher association between history of atrial ablation and left atrial calcification compared with rheumatic heart disease, suggesting a potential shift in etiology. [ABSTRACT FROM AUTHOR]

Objectives: To evaluate the effectiveness of a novel artificial intelligence (AI) algorithm for fully automated measurement of left atrial (LA) volumes and function using cardiac CT in patients with atrial fibrillation. Methods: We included 79 patients (mean age 63 ± 12 years; 35 with atrial fibrillation (AF) and 44 controls) between 2017 and 2020 in this retrospective study. Images were analyzed by a trained AI algorithm and an expert radiologist. Left atrial volumes were obtained at cardiac end-systole, end-diastole, and pre-atrial contraction, which were then used to obtain LA function indices. Intraclass correlation coefficient (ICC) analysis of the LA volumes and function parameters was performed and receiver operating characteristic (ROC) curve analysis was used to compare the ability to detect AF patients. Results: The AI was significantly faster than manual measurement of LA volumes (4 s vs 10.8 min, respectively). Agreement between the manual and automated methods was good to excellent overall, and there was stronger agreement in AF patients (all ICCs ≥ 0.877; p < 0.001) than controls (all ICCs ≥ 0.799; p < 0.001). The AI comparably estimated LA volumes in AF patients (all within 1.3 mL of the manual measurement), but overestimated volumes by clinically negligible amounts in controls (all by ≤ 4.2 mL). The AI's ability to distinguish AF patients from controls using the LA volume index was similar to the expert's (AUC 0.81 vs 0.82, respectively; p = 0.62). Conclusion: The novel AI algorithm efficiently performed fully automated multiphasic CT-based quantification of left atrial volume and function with similar accuracy as compared to manual quantification. Summary statement: Novel CT-based AI algorithm efficiently quantifies left atrial volumes and function with similar accuracy as manual quantification in controls and atrial fibrillation patients. Key Points: • There was good-to-excellent agreement between manual and automated methods for left atrial volume quantification. • The AI comparably estimated LA volumes in AF patients, but overestimated volumes by clinically negligible amounts in controls. • The AI's ability to distinguish AF patients from controls was similar to the manual methods. [ABSTRACT FROM AUTHOR]

Background: Airspace disease as seen on chest X-rays is an important point in triage for patients initially presenting to the emergency department with suspected COVID-19 infection. The purpose of this study is to evaluate a previously trained interpretable deep learning algorithm for the diagnosis and prognosis of COVID-19 pneumonia from chest X-rays obtained in the ED.Methods: This retrospective study included 2456 (50% RT-PCR positive for COVID-19) adult patients who received both a chest X-ray and SARS-CoV-2 RT-PCR test from January 2020 to March of 2021 in the emergency department at a single U.S.Institution: A total of 2000 patients were included as an additional training cohort and 456 patients in the randomized internal holdout testing cohort for a previously trained Siemens AI-Radiology Companion deep learning convolutional neural network algorithm. Three cardiothoracic fellowship-trained radiologists systematically evaluated each chest X-ray and generated an airspace disease area-based severity score which was compared against the same score produced by artificial intelligence. The interobserver agreement, diagnostic accuracy, and predictive capability for inpatient outcomes were assessed. Principal statistical tests used in this study include both univariate and multivariate logistic regression.Results: Overall ICC was 0.820 (95% CI 0.790-0.840). The diagnostic AUC for SARS-CoV-2 RT-PCR positivity was 0.890 (95% CI 0.861-0.920) for the neural network and 0.936 (95% CI 0.918-0.960) for radiologists. Airspace opacities score by AI alone predicted ICU admission (AUC = 0.870) and mortality (0.829) in all patients. Addition of age and BMI into a multivariate log model improved mortality prediction (AUC = 0.906).Conclusion: The deep learning algorithm provides an accurate and interpretable assessment of the disease burden in COVID-19 pneumonia on chest radiographs. The reported severity scores correlate with expert assessment and accurately predicts important clinical outcomes. The algorithm contributes additional prognostic information not currently incorporated into patient management. [ABSTRACT FROM AUTHOR]

Crop yield estimation from satellite data requires field observations to fit and evaluate predictive models. However, it is not clear how much field data collection methods matter for predictive performance. To evaluate this, we used maize yield estimates obtained with seven field methods (two farmer estimates, two point transects, and three crop cut methods) and the "true yield" measured from a full-field harvest for 196 fields in three districts in Ethiopia in 2019. We used a combination of nine vegetation indices and five temporal aggregation methods for the growing season from Sentinel-2 SR data as yield predictors in the linear regression and Random Forest models. Crop-cut-based models had the highest model fit and accuracy, similar to that of full-field-harvest-based models. When the farmer estimates were used as the training data, the prediction gain was negligible, indicating very little advantage to using remote sensing to predict yield when the training data quality is low. Our results suggest that remote sensing models to estimate crop yield should be fit with data from crop cuts or comparable high-quality measurements, which give better prediction results than low-quality training data sets, even when much larger numbers of such observations are available. [ABSTRACT FROM AUTHOR]

Rural development objectives are often framed relative to a targeted number of beneficiary farms and farm households. Yet the data available on the number and distribution of the world's farms has been surprisingly fragmented and coherent estimates of the number of farms in a given region for a given year have not been available. We take a set of simple rules to use existing data sources to generate a harmonized set of farm number estimates at the national level. We estimate there are 656 million farms globally in 2020, with a projected decline to 624 million farms globally by 2030. These estimates can be used to better inform policy and large-scale investment programming and design. We also articulate the need for further investments in basic agricultural census data, and outline an agenda for the generation of farm distribution data that would be most useful for further policy guidance. [ABSTRACT FROM AUTHOR]

We use nationally representative survey data from Zambia to evaluate whether rural youth out-migration is associated with a lack of rural economic vibrancy, as often asserted in policy discussion. After controlling for distance from markets, we do find some evidence that areas with more productive and market-oriented agriculture are associated with lower rates of out-migration, particularly for those who leave explicitly to find work. However, measures of non-farm orientation of the local economy are positively associated with out-migration, indicating that the sectoral composition of the local economy has some bearing on the relationship between vibrancy and migration. When we examine more disaggregated patterns, we find that the out-migration decisions of young males are particularly responsive to rural vibrancy conditions. However, we find that the migration decisions of those who have completed secondary school are influenced less by vibrancy measures than the decisions of less educated individuals, possibly indicating that individuals who aspire to leave rural areas, and who invest in educational attainment to achieve such aspirations, may be less susceptible to influence by rural productivity changes. Our results suggest that the narrative of a stagnation-driven exodus of educated rural youth is oversimplified and, as such, is of limited policy relevance. [ABSTRACT FROM AUTHOR]

Background: Artificial intelligence (AI) in diagnostic radiology is undergoing rapid development. Its potential utility to improve diagnostic performance for cardiopulmonary events is widely recognized, but the accuracy and precision have yet to be demonstrated in the context of current screening modalities. Here, we present findings on the performance of an AI convolutional neural network (CNN) prototype (AI-RAD Companion, Siemens Healthineers) that automatically detects pulmonary nodules and quantifies coronary artery calcium volume (CACV) on low-dose chest CT (LDCT), and compare results to expert radiologists. We also correlate AI findings with adverse cardiopulmonary outcomes in a retrospective cohort of 117 patients who underwent LDCT.Methods: A total of 117 patients were enrolled in this study. Two CNNs were used to identify lung nodules and CACV on LDCT scans. All subjects were used for lung nodule analysis, and 96 subjects met the criteria for coronary artery calcium volume analysis. Interobserver concordance was measured using ICC and Cohen's kappa. Multivariate logistic regression and partial least squares regression were used for outcomes analysis.Results: Agreement of the AI findings with experts was excellent (CACV ICC = 0.904, lung nodules Cohen's kappa = 0.846) with high sensitivity and specificity (CACV: sensitivity = .929, specificity = .960; lung nodules: sensitivity = 1, specificity = 0.708). The AI findings improved the prediction of major cardiopulmonary outcomes at 1-year follow-up including major adverse cardiac events and lung cancer (AUCMACE = 0.911, AUCLung Cancer = 0.942).Conclusion: We conclude the AI prototype rapidly and accurately identifies significant risk factors for cardiopulmonary disease on standard screening low-dose chest CT. This information can be used to improve diagnostic ability, facilitate intervention, improve morbidity and mortality, and decrease healthcare costs. There is also potential application in countries with limited numbers of cardiothoracic radiologists. [ABSTRACT FROM AUTHOR]

Photon-counting-detector computed tomography (PCD-CT) offers enhanced noise reduction, spatial resolution, and image quality in comparison to energy-integrated-detectors CT (EID-CT). These hypothesized improvements were compared using PCD-CT ultra-high (UHR) and standard-resolution (SR) scan-modes. Phantom scans were obtained with both EID-CT and PCD-CT (UHR, SR) on an adult body-phantom. Radiation dose was measured and noise levels were compared at a minimum achievable slice thickness of 0.5 mm for EID-CT, 0.2 mm for PCD-CT-UHR and 0.4 mm for PCD-CT-SR. Signal-to-noise ratios (SNR) and contrast-to-noise ratios (CNR) were calculated for five tissue densities. Additionally, data from 25 patients who had PCD-CT of chest were reconstructed at 1 mm and 0.2 mm (UHR) slice-thickness and compared quantitatively (SNR) and qualitatively (noise, quality, sharpness, bone details). Phantom PCD-CT-UHR and PCD-CT-SR scans had similar measured radiation dose (16.0mGy vs 15.8 mGy). Phantom PCD-CT-SR (0.4 mm) had lower noise level in comparison to EID-CT (0.5 mm) (9.0HU vs 9.6HU). PCD-CT-UHR (0.2 mm) had slightly higher noise level (11.1HU). Phantom PCD-CT-SR (0.4 mm) had higher SNR in comparison to EID-CT (0.5 mm) while achieving higher resolution (Bone 115 vs 96, Acrylic 14 vs 14, Polyethylene 11 vs 10). SNR was slightly lower across all densities for PCD-CT UHR (0.2 mm). Interestingly, CNR was highest in the 0.2 mm PCD-CT group; PCD-CT CNR was 2.45 and 2.88 times the CNR for 0.5 mm EID-CT for acrylic and poly densities. Clinical comparison of SNR showed predictably higher SNR for 1 mm (30.3 ± 10.7 vs 14.2 ± 7, p = 0.02). Median subjective ratings were higher for 0.2 mm UHR vs 1 mm PCD-CT for nodule contour (4.6 ± 0.3 vs 3.6 ± 0.1, p = 0.02), bone detail (5 ± 0 vs 4 ± 0.1, p = 0.001), image quality (5 ± 0.1 vs 4.6 ± 0.4, p = 0.001), and sharpness (5 ± 0.1 vs 4 ± 0.2). Both UHR and SR PCD-CT result in similar radiation dose levels. PCD-CT can achieve higher resolution with lower noise level in comparison to EID-CT. • Photon counting CT ultra-high resolution provides higher resolution with comparable radiation. • Photon counting CT ultra-high resolution outperforms Energy-Integrating CT in noise reduction and image quality. • Ultra-high resolution photon counting CT offers increased sharpness, detail compared to standard reconstructions. • Ultra-high resolution photon counting CT has significantly higher contrast to noise ratio. • Clinical study confirms improved sharpness and image quality with ultra-high resolution photon counting CT. [ABSTRACT FROM AUTHOR]

We present an easily calibrated spatial modeling framework for estimating location-specific fertilizer responses, using smallholder maize farming in Tanzania as a case study. By incorporating spatially varying input and output prices, we predict the expected profitability for a location-specific smallholder farmer. A stochastic rainfall component of the model allows us to quantify the uncertainty around expected economic returns. The resulting mapped estimates of expected profitability and uncertainty are good predictors of actual smallholder fertilizer usage in nationally representative household survey data. The integration of agronomic and economic information in our framework makes it a powerful tool for spatially explicit targeting of agricultural technologies and complementary investments, as well as estimating returns to investments at multiple scales. [ABSTRACT FROM AUTHOR]

Given the marked heterogeneous conditions in smallholder agriculture in Sub‐Saharan Africa, there is a growing policy interest in site‐specific extension advice and the use of digital extension tools to provide site‐specific information. Empirical ex‐ante studies on the design of digital extension tools and their use are rare. Using data from a choice experiment in Nigeria, we elicit and analyze the preferences of extension agents for major design features of ICT‐enabled decision support tools (DSTs) aimed at site‐specific nutrient management extension advice. We estimate different models, including mixed logit, latent class and attribute non‐attendance models. We find that extension agents are generally willing to use such DSTs and prefer a DST with a more user‐friendly interface that requires less time to generate results. We also find that preferences are heterogeneous: some extension agents care more about the effectiveness‐related features of DSTs, such as information accuracy and level of detail, while others prioritise practical features, such as tool platform, language and interface ease‐of‐use. Recognising and accommodating such preference differences may facilitate the adoption of DSTs by extension agents and thus enhance the scope for such tools to impact the agricultural production decisions of farmers. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Agricultural extension to improve yields of staple food crops and close the yield gap in Sub-Saharan Africa often entails general recommendations on soil fertility management that are distributed to farmers in a large growing area. Site-specific extension recommendations that are better tailored to the needs of individual farmers and fields, and enabled by digital technologies, could potentially bring about yield and productivity improvements. In this paper, we analyze farmers' preferences for high-input maize production supported by site-specific nutrient management recommendations provided by an ICT-based extension tool that is being developed for extension services in the maize belt of Nigeria. We use a choice experiment to provide ex-ante insights on the adoption potentials of site-specific extension services from the perspective of farmers. We control for attribute non-attendance and account for class as well as scale heterogeneity in preferences using different models, and find robust results. We find that farmers have strong preferences to switch from general to ICT-enabled site-specific soil fertility management recommendations which lend credence to the inclusion of digital technologies in agricultural extension. We find heterogeneity in preferences that is correlated with farmers' resource endowments and access to services. A first group of farmers are strong potential adopters; they are better-off, less sensitive to risk, and are more willing to invest in a high-input maize production system. A second group of farmers are weak potential adopters; they have lower incomes and fewer productive assets, are more sensitive to yield variability, and prefer less capital and labor intensive production techniques. Our empirical findings imply that improving the design of extension tools to enable provision of information on the riskiness of expected outcomes and flexibility in switching between low-risk and high-risk recommendations will help farmers to make better informed decisions, and thereby improve the uptake of extension advice and the efficiency of extension programs. • Choice experiement on farmer preferences for site-specific extension recommendations • High willingness to accept ICT-enabled and site-specific extension recommendations • Observed heterogeneity in farmers' willingness to intensify production systems • Information on riskiness of return crucial for improved design of extension tools [ABSTRACT FROM AUTHOR]

Despite the continued deep challenges that the region is facing, mounting evidence points to profound economic transformation in sub-Saharan Africa since the early 2000s. The contributions in this special issue highlight three aspects of Africa's unfolding economic transformation since 2000: remarkable progress for the region as a whole, highly uneven progress across countries, and unresolved questions about the sustainability of the transformations. The drivers of the region's economic transformations are diverse, and include improved governance, strong agricultural growth in some countries, employment expansion in informal rural off-farm activities, strong local and foreign investment, a period of high global commodity prices, and policy reforms undertaken in earlier decades. Agricultural growth, by expanding job opportunities in the non-farm sectors through multiplier effects, is likely to remain an important driver of continued transformation, though it will increasingly need to rely on productivity growth rather than area expansion. [ABSTRACT FROM AUTHOR]

Background: Evaluation for gastrointestinal leak is a frequent imaging indication, and dual-energy computed tomography (DECT) with oral or rectally administered contrast can be used to improve efficiency and diagnostic confidence. Purpose: To assess the value of the DECT iodine overlay (IO) reconstruction as a stand-alone image set compared to routine CT in assessing oral or rectal contrast leak from the gastrointestinal system. Material and Methods: A blinded, retrospective audit study was performed by three readers who each interpreted 50 studies performed for assessment of oral or rectal contrast leak that were acquired using DECT. Each reader independently assessed both the routine CT images and the images of the reconstructed IO for contrast leak in random order with a six-week "wash-out period" between readings. Clinical follow-up provided the reference standard. Readers recorded the presence/absence of a leak, diagnostic confidence, image quality score, and interpretation time for each image set. Results: Pooled data for overall accuracy in identification of a leak increased from 0.81 (95% confidence interval [CI]=0.74–0.87) for routine CT to 0.91 (95% CI=0.85–0.95) with IO, and the area under the curve (AUC) was significantly higher for IO than routine CT (P = 0.015). Readers required significantly less time to interpret IO than routine CT (median improvement of 12.5 s per image using pooled data; P < 0.001) while maintaining diagnostic confidence and perceived image quality. Conclusion: Use of DECT IO reconstructions for identification of oral or rectal contrast leak requires less time to interpret than routine CT with improved accuracy and maintained diagnostic confidence and perceived image quality. [ABSTRACT FROM AUTHOR]

Single-photon-emission-computerized-tomography/computed-tomography(SPECT/CT) is commonly used for pulmonary disease. Scant work has been done to determine ability of AI for secondary findings using low-dose-CT(LDCT) attenuation correction series of SPECT/CT. 120 patients with ventilation-perfusion-SPECT/CT from 9/1/21–5/1/22 were included in this retrospective study. AI-RAD companion(VA10A,Siemens-Healthineers, Erlangen, Germany), an ensemble of deep-convolutional-neural-networks was evaluated for the detection of pulmonary nodules, coronary artery calcium, aortic ectasia/aneurysm, and vertebral height loss. Accuracy, sensitivity, specificity was measured for the outcomes. Inter-rater reliability were measured. Inter-rater reliability was measured using the intraclass correlation coefficient (ICC) by comparing the number of nodules identified by the AI to radiologist. Overall per-nodule accuracy, sensitivity, and specificity for detection of lung nodules were 0.678(95%CI 0.615–0.732), 0.956(95%CI 0.900–0.985), and 0.456(95%CI 0.376–0.543), respectively, with an intraclass correlation coefficient (ICC) between AI and radiologist of 0.78(95%CI 0.71–0.83). Overall per-patient accuracy for AI detection of coronary artery calcium, aortic ectasia/aneurysm, and vertebral height loss was 0.939(95%CI 0.878–0.975), 0.974(95%CI 0.925–0.995), and 0.857(95%CI 0.781–0.915), respectively. Sensitivity for coronary artery calcium, aortic ectasia/aneurysm, and vertebral height loss was 0.898(95%CI 0.778–0.966), 1 (95%CI 0.958–1), and 1 (95%CI 0.961–1), respectively. Specificity for coronary artery calcium, aortic ectasia/aneurysm, and vertebral height loss was 0.969(95% CI 0.893–0.996), 0.897 (95% CI 0.726–0.978), and 0.346 (95% CI 0.172–0.557), respectively. AI ensemble was accurate for coronary artery calcium and aortic ectasia/aneurysm, while sensitive for aortic ectasia/aneurysm, lung nodules and vertebral height loss on LDCT attenuation correction series of SPECT/CT. • Artificial intelligence identifies pulmonary nodules, coronary artery calcium, aortic ectasia/aneurysm, and vertebral height loss on low-dose CT of SPECT/CT. • Use of deep learning on low-dose CT series of ventilation and perfusion SPECT/CT imaging for the identification of incidental findings. • Artificial intelligence identifies pulmonary nodules on low-dose CT series of SPECT/CT. • Artificial intelligence screens for coronary artery calcium and aortic ectasia/aneurysm on low-dose CT series of SPECT/CT. [ABSTRACT FROM AUTHOR]

Maize sector policies in eastern and southern Africa are characterized by a large and often growing presence of the state. Yet the scope, scale, and modalities of state activities vary substantially across countries. Drawing on data from Malawi, Zambia, Kenya, and Mozambique this article compares the relative degree of state intervention in the maize sector. We show that relative preferences for output market subsidies, input market subsidies, trade restrictions, or non-interventionist approaches reflect the interplay of interest group lobbying, patronage networks, and ethnic and regional political affiliations. These relationships have deep historical roots and have often been intensified in the context of the emergence of multiparty politics. We show that interventionist orientations in output markets and trade do not translate into better performance or welfare outcomes. Input subsidy preferences produce more ambiguous welfare results, when the opportunity costs are not fully accounted for. [ABSTRACT FROM AUTHOR]

Smallholder agriculture in sub-Saharan Africa is commonly characterised by high levels of technical inefficiency. However, much of this characterisation relies on self-reported input and production data, which are prone to systematic measurement error. We show theoretically that non-classical measurement error introduces multiple identification challenges and sources of bias in estimating smallholders' technical inefficiency. We then empirically examine the implications of measurement error for the estimation of technical inefficiency using smallholder farm survey data from Ethiopia, Malawi, Nigeria and Tanzania. We find that measurement error in agricultural input and production data leads to a substantial upward bias in technical inefficiency estimates (by up to 85% for some farmers). Our results suggest that existing estimates of technical efficiency in sub-Saharan Africa may be severe underestimates of smallholders' actual efficiency and what is commonly attributed to farmer inefficiency may be an artefact of mismeasurement in agricultural data. Our results raise questions about the received wisdom on African smallholders' production efficiency and prior estimates of the productivity of agricultural inputs. Improving the measurement of agricultural data can improve our understanding of smallholders' production efficiencies and improve the targeting of productivity-enhancing technologies. [ABSTRACT FROM AUTHOR]

We combine nationally representative data from Nigeria with spatiotemporal data from remote sensing and other sources to study how young migrants respond to observable characteristics of potential destinations, both in absolute terms and relative to origin locations. Migrants prefer destinations with better welfare, land availability and intensity of economic activity. We also find that migrants prefer shorter distances and those destinations with better urban amenities and infrastructure. However, responses vary by type of migrant and migration. For example, rural-rural migrants are more responsive to land availability and agricultural potential, while rural-urban and urban-urban migrants are more responsive to welfare and economic vibrancy (measured by nightlight intensity) in destinations. Distance induces varying impact on migration choices of poor and non-poor migrants as well as across more educated and less educated migrants. Longer distances discourage migration for female migrants, poorer migrants, and less educated migrant while the implication for the non-poor and more educated migrants appears to be negligible. This is intuitive because poorer and less educated migrants have liquidity constraints to finance high migration costs. Our results suggest potential scope for predicting how labour mobility responds to alternative regional development policies. [ABSTRACT FROM AUTHOR]

Summary Zambia’s land policies privilege agricultural land alienation by wage earners over local smallholder farmers. As a result, land title holders are not statistically significantly different from non-title holders in terms of agricultural productivity, despite greater access to investment capital from wage earnings and higher levels of education. The effects of land titling on long- and medium-term land investments are also limited. These results are indicative of widespread speculative alienation of agricultural land by wage earners. Enabling more productive land users to acquire title requires policy change aimed at lowering the transaction costs and bureaucratic complexity of acquiring land title. [ABSTRACT FROM AUTHOR]

Evidence assembled in this special issue of Food Policy shows that rising rural population densities in parts of Africa are profoundly affecting farming systems and the region's economies in ways that are underappreciated in current discourse on African development issues. This study synthesizes how people, markets and governments are responding to rising land pressures in Africa, drawing on key findings from the various contributions in this special issue. The papers herein revisit the issue of Boserupian agricultural intensification as an important response to land constraints, but they also go further than Boserup and her followers to explore broader responses to land constraints, including non-farm diversification, migration, and reduced fertility rates. Agricultural and rural development strategies in the region will need to more fully anticipate the implications of Africa's rapidly changing land and demographic situation, and the immense challenges that mounting land pressures pose in the context of current evidence of unsustainable agricultural intensification, a rapidly rising labor force associated with the region's current demographic conditions, and limited nonfarm job creation. These challenges are manageable but will require explicit policy actions to address the unique development challenges in densely populated rural areas. [ABSTRACT FROM AUTHOR]

Sub-Saharan Africa is typically regarded as land abundant, and previous efforts to estimate the true extent of potentially available cropland (PAC) have largely affirmed this perception. Such efforts, however, have largely focused on production potential and have underemphasized economic profitability and other constraints to expansion. This paper re-estimates PAC for Africa in a more explicit economic framework that emphasizes the returns to agricultural production under a variety of assumptions, using recent geospatial data. Existing PAC estimates for Africa are shown to be highly sensitive to assumptions about land productivity and market access, and are moderately influenced by the use of alternative data sources. The region's underutilized land resources are concentrated in relatively few countries, many of which are fragile states. Between one-half and two-thirds of the region's surplus land is currently under forest cover; conversion of forests to cropland would entail major environmental costs. Most of the continent's unexploited land resources are located far from input and output markets, limiting their economic attractiveness. In the long run, improvements in infrastructure and agricultural productivity and the growth of hinterland towns will enhance the economic returns to cropland expansion. In the short to medium term, however, the potential for profitable smallholder-based cropland expansion in most African countries is likely to be much more limited than it is typically perceived to be. [ABSTRACT FROM AUTHOR]

Summary: This paper critically examines the dimensions of market access. Using farm panel survey data from Kenya, we investigate changes in ten indicators of market access during the 1997–2010 post-liberalization period. We find major improvements in certain market access conditions and little change in others. There is generally low correlation between alternative indicators of market access. All-purpose indicators may not accurately reflect specific market access conditions of interest. Distances to physical infrastructure are often not representative of farmers’ access to markets; changes in specific market access conditions may have more to do with the behaviors of marketing agents. We consider the implications of these findings for agricultural marketing policies and for research methods. [ABSTRACT FROM AUTHOR]

Acid soils are a major constraint to agricultural productivity in many parts of sub-Saharan Africa, including Ethiopia. Restoring soil pH to optimal ranges for agriculture can have a significant impact on yields, particularly for acid intolerant crops like wheat and barley. The application of agricultural lime is the standard corrective, although the large application requirements, lack of farmer awareness, and weak or non-existent lime supply chains make this a complex problem to address at scale. To date, no large-scale farmer trials of lime application have been undertaken in Ethiopia. This leaves open the question to local policy makers as to the economic benefits given the enormous capital and logistics investments required. To help address this we leverage existing spatial edaphic data and longitudinal crop surveys to simulate the productivity impact of varying lime and fertilizer applications. Our estimates find the impact of moving pH from 5.5 to 6.5, modeled as a lime soil remediation strategy, increases yields by 22% and 19% for wheat and barley, respectively. In addition, at lower pH levels our models indicate that commonly used nitrogen-based fertilizers are less cost-effective. For wheat in highly acidic soils, we find that fertilizers cost over two times as much as a single application of lime over a five-year period. The cost savings of the use of lime reaches as high as 121% of average one-year agricultural household income for wheat; with barley these savings are lower but still substantial at 24%. In general, we advocate for an integrated soil fertility management strategy that applies appropriate levels of fertilizer on pH balanced soil. If successful, Ethiopia's acid soil reclamation could become a modest version of Brazil's successful "cerrado miracle" and serve as an example for Africa. [ABSTRACT FROM AUTHOR]

Maize production must increase three-fold in sub-Saharan Africa to meet the needs of future generations. Sustainably increasing maize production will require a portfolio of complementary technologies and policies. In this review we highlight four challenges to achieve this improvement. Improved nutritional density of maize in farmers' fields and increased adoption of new varieties and faster varietal replacement. Increased fertilizer use should increase maize yields but there is increasing evidence that low and variable returns on investment can limit uptake. Finally, adoption of new technologies is not uniform, with lower rates of adoption by female farmers. Without addressing gender specific challenges to adoption, technologies will never achieve their desired impacts. Given the scope of the challenge, strategies to improve maize productivity must overcome these constraints as part of an integrative approach. [Display omitted] • Biofortification targets need to consider soil fertility gradients. • Barriers exist for business to successfully push new products onto the market. • Inclusion of consumer preferred traits may increase adoption. • Attainable yield of new varieties depends on complementary management practices. • Technology adoption constraints are context-specific and gendered. [ABSTRACT FROM AUTHOR]

Abstract: Of Malawi’s rural population, 66% have a level of consumption below the national poverty line. We examine the spatial determinants of the prevalence of poverty for small spatially defined populations there. A theoretical approach based on the risk-chain conceptualization of household economic vulnerability guided selection of a set of potential risks and coping strategies – our analytical determinants – that could be represented spatially. We used these to develop global and local models of poverty prevalence. In our global spatial error model, only eight of 24 determinants selected for analysis proved significant. In contrast, all determinants considered were significant in at least some of the local models developed using geographically weighted regression. Moreover, these models provided strong evidence of the spatial non-stationarity of the relationship between poverty and its determinants. This result implies that poverty reduction efforts in rural Malawi should be designed for and targeted at district and subdistrict levels. [Copyright &y& Elsevier]

• Accurate measures of yield are essential for evaluating agricultural productivity. • Substantial heterogeneity in yield estimation protocols has received little critical attention. • Alternative protocols differ widely in yield estimation accuracy. • Crop cut from a single, randomly selected octant outperforms other protocols in our study. • All yield estimation methods in our study suffer from non-classical measurement error. Agricultural statistics and applied analyses have benefitted from moving from farmer estimates of yield to crop cut based estimates, now regarded as a gold standard. However, in practice, crop cuts and other sample-based protocols vary widely in the details of their implementations and little empirical work has documented how alternative yield estimation methods perform. Here, we undertake a well-measured experiment of multiple yield estimation methods on 237 smallholder maize plots in Amhara region, Ethiopia. We compare yield from a full plot harvest with farmer assessments and with estimates from a variety of field sampling protocols: W-walk, transect, random quadrant, random octant, center quadrant, and 3 diagonal quadrants. We find that protocol choices are important: alternative protocols vary considerably in their accuracy relative to the whole plot, with absolute mean errors ranging from 23 (farmer estimates) to 10.6 (random octant). Furthermore, while most methods approximate the sample mean reasonably well, the divergence of individual measures from true plot-level values can be considerable. We find that randomly positioned quadrants outperform systematic sampling schemes: the random octant had the best accuracy and was the most cost-effective. The nature of bias is non-classical: bias is correlated with plot size as well as with plot management characteristics. In summary, our results advocate that even "gold standard" crop cut measures should be interpreted cautiously, and more empirical work should be carried out to validate and extend our conclusions. [ABSTRACT FROM AUTHOR]

• We examine inter- and intra-plot heterogeneity of productivity in maize plots. • We find high inter- and intra-plot variabilities in plant population and yield. • Intra-plot heterogeneity overrides inter-plot heterogeneity in smallholder maize. • The magnitude and distribution of yield estimation errors are method-dependent. • More intensively managed plots show lower intra-plot heterogeneity and lower yield. Intra-plot heterogeneity in yield is often observed in smallholder farming systems, although its implications for yield measurement remain under-investigated. Using a unique dataset on smallholder maize production in Ethiopia, we quantify the magnitude of inter- and intra-plot heterogeneity, describe the relationship between intra-plot heterogeneity and maize productivity, and document the implications of intra-field heterogeneity on the accuracy of alternative yield estimation protocols. Our data include five common yield estimation protocols, as well as full plot harvests of 230 smallholder maize fields. We surveyed agronomic decisions, biophysical variables, and accessibility characteristics of the surveyed fields. We quantify intra-plot heterogeneity using the coefficient of variation (CV) of stand density, cob weight, and maize grain yield. A generalized linear mixed model is used to explore the relationship between these variables and the method- and heterogeneity-dependence of yield estimation accuracy. We find inter-plot CV values ranging from 32 to 56 %, 22 to 73 % and 39 to 49 % in population density, cob weight and grain yield, respectively. Intra-plot heterogeneity constituted most of this variation, with across-method mean CV values of 41 %, 82 % and 63 %, respectively, of the total variability in population density, cob weight and grain yield. A rise in intra-plot heterogeneity of 0.5 % to 0.8 % is associated with a significant increase in yield estimation error under alternative yield estimation protocols. Regression analysis shows that interactions in agronomic decisions, input intensity and plot accessibility factors dictate intra-plot heterogeneity and method accuracy in smallholder systems. Intra-plot heterogeneity is larger than inter-plot heterogeneity in the current study area. Our analysis shows that the effect of intra-plot heterogeneity on yield estimation accuracies is method-dependent and yield estimation methods that fail to capture true intra-plot heterogeneity are more error-prone. Results of such estimations should be considered with caution when used as the basis of decision-making. [ABSTRACT FROM AUTHOR]

[Display omitted] • Two calibrations of QUEFTS with different outlier selection methods were compared. • QUEFTS could not be calibrated meaningfully with data diverging from expected correlations. • Quantile regression was tested as an alternative approach to adapt QUEFTS. • QUEFTS-based quantile regression can predict attainable yields. • When yield is not only limited by nutrients, predicting yield ranges is more meaningful. Understanding yield responses to nutrient application is a key input for extension advice and strategic agricultural investments in developing countries. A commonly used model for yield responses to nutrient inputs in tropical smallholder farming systems is QUEFTS (QUantitative Evaluation of the Fertility of Tropical Soils). While QUEFTS has a strong conceptual foundation, a key assumption is that nutrients are the only limiting factors. One implication of this is the required assumption of 'perfect management'. This may be problematic in the application of QUEFTS in smallholder farming systems with a wide variety of yield limiting factors. In a previous study, QUEFTS was calibrated using farm trials in two major maize production zones in Nigeria. To reduce observed variability in correlations between estimated soil nutrient (N, P, K) supply and soil parameters (e.g. soil organic carbon, soil pH; step 1 of QUEFTS) a Mahalanobis distance method was used to remove data points not adhering to expected correlations. In this study, we assessed an alternative approach: can the QUEFTS model be adapted to fit smallholder farming systems and associated variation in management? Using 676 observations from the same nutrient omission trials in two major maize production zones in Nigeria, we compare a standard linear regression approach with a quantile regression approach to calibrate QUEFTS. We find that under the standard linear regression approach, there is a poor relation between predicted and observed yields. Using quantile regression, however, QUEFTS performed better at predicting attainable yields – defined as the 90th percentile of observed yields – under a wide variety of production conditions. Our results indicate that using quantile regression as a way to predict attainable yields, is a useful alternative implementation of QUEFTS in smallholder farming systems with high variability in management and other characteristics. [ABSTRACT FROM AUTHOR]

We use a unique dataset from Malawi that matches tenants and their landlord counterparts to document the role played by absentee tenants, i.e. tenants who reside outside the area where the rented land is located. We found that non-local tenants made up 22% of the tenants in our sample. A significant subset of them had higher off-farm income and significantly more assets than did other tenants. Conversely, we found that 76% of landlords rented land because they needed cash. Our results highlight the fact that some rental transactions reinforce power imbalances and may exacerbate risks faced by poorer landlords. [ABSTRACT FROM AUTHOR]

• Local farm size distributions affect the income levels of rural Tanzanian households. • Farms operating 5–10 ha generate positive income effects for nearby households. • However, poor households benefit the least from proximity to 5–10 ha farms. • Proximity to farms operating over 20 ha generates little or no positive income effects. Many African countries have recently experienced rapid growth in the numbers of medium- and large-scale farms. These developments have generated considerable speculation about the impacts of farmland concentration and inequality on smallholder households and communities. This study exploits inter-district variation in farm landholding patterns in Tanzania to determine how differences in localized farmland structure affect rural household incomes using nationally representative household panel survey data. Because farm structure is a multifaceted concept, five alternative indicators of farmland structure are defined for 142 districts in Tanzania: (i) the Gini coefficient; (ii) skewness; (iii) coefficient of variation; (iv) share of controlled farmland under medium-scale farms; and (v) share of controlled farmland under large farms. These alternative farm structure variables are included in models of rural household income to test their effects after controlling for available household and community covariates. The study highlights four main findings. First, most indicators of farmland concentration are positively associated with rural household incomes. Second, household incomes from farm and non-farm sources are positively and significantly associated with the share of land in the district controlled by farms in the 5–10 and 5–20 ha category. Third, these positive spillover benefits are smaller and less statistically significant in districts with a relatively high share of farmland controlled by farms over 20 ha in size. Fourth, poor rural households are least able to capture the positive spillovers generated by medium-scale farms and by concentrated farmland patterns. [ABSTRACT FROM AUTHOR]

Bisbenzimidazoles with terminal alkynyl linkers, selective inhibitors of bacterial topoisomerase I, have been evaluated using bacterial cytological profiling (BCP) to ascertain their mechanism of action and screened for synergism to improve Gram-negative bacterial coverage. Principal component analysis of high throughput fluorescence images suggests a dual-mechanism of action affecting DNA synthesis and cell membrane integrity. Fluorescence microscopy of bacteria challenged with two of the alkynyl-benzimidazoles revealed changes in the cellular ultrastructure that differed from topoisomerase II inhibitors including induction of spheroplasts and membrane lysis. The cytoskeleton recruitment enzyme inhibitor A22 in combination with one of the alkynyl-benzimidazoles was synergistic against Acinetobacter baumannii and Escherichia coli. Gram-positive coverage remained unchanged in the A22-alkynyl bisbenzimidazole combination. Efflux inhibitors were not synergistic, suggesting that the Gram-negative outer membrane was a significant barrier for alkynyl-bisbenzimidazole uptake. Time-kill assays demonstrated the A22-bisbenzimidazole combination had a similar growth inhibition curve to that of norfloxacin in E.coli. Bisbenzimidazoles with terminal alkynyl linkers likely impede bacterial growth by compromising cell membrane integrity and by interfering with DNA synthesis against Gram-positive pathogens and in the synergistic combination against Gram-negative pathogens including E. coli and multidrug-resistant A. baumanii. [ABSTRACT FROM AUTHOR]

• We appraise evidence on the uptake of decision support tools for site-specific nutrient management (SSNM-DSTs). • SSNM-DSTs still struggle to reach scale, despite evidence of benefits. • Scaling constraints vary by type of tool and user characteristics. • SSNM-DST address in-field heterogeneity, but not socioeconomic heterogeneity of users. • Future work should include institutional, economic and governance aspects of scaling. While many have extolled the potential impacts of digital advisory services for smallholder agriculture, the evidence for sustained uptake of such tools remains limited. This paper utilizes a survey of tool developers and researchers, as well as a systematic meta -analysis of prior studies, to assess the extent and challenges of scaling decision support tools for site-specific soil nutrient management (SSNM-DST) across smallholder farming systems, where "scaling" is defined as a significant increase in tool usage beyond pilot levels. Our evaluation draws on relevant literature, expert opinion and apps available in different repositories. Despite their acclaimed yield benefits, we find that SSNM-DST have struggled to reach scale over the last few decades and, with strong heterogeneity in adoption among intended stakeholders and tools. For example, the log odds of a SSNM-DST reaching 5–10 % of the target farmers compared with reaching none, decreases by ∼200% when a technical problem is stated as a reason for the tools' failure to be used at scale. We find a similar decrease in odds ratios when technical, socioeconomic, policy, and R&D constraints were identified as barriers to scaling by national extension and private systems. Meta-regression analysis indicates that the response ratio of using SSNM-DST over Farmer Fertilizer Practice (FFP) varies by non-tool related covariates, such as initial crop yield potential under FFP, current and past crop types, acidity class of the soil, temperature and rainfall regimes, and the amount of input under FFP. In general, the SSNM-DST have moved one step forward compared with the traditional 'blanket' fertilizer recommendation by accounting for in-field heterogeneities in soil and crop characteristics, while remaining undifferentiated in terms of demographic and socioeconomic heterogeneities among users, which potentially constrains adoption at scale. The SSNM-DSTs possess reasonable applicability and can be labeled 'ready' from purely scientific viewpoints, although their readiness for system-level uptake at scale remains limited, especially where socio-technical and institutional constraints are prevalent. [ABSTRACT FROM AUTHOR]

• Enthusiasm for the potential of ICTs to transform agricultural markets in Africa has motivated considerable investment in market-oriented digital tool development. • However, progress made thus far has been limited to small-scale experiments that often fail to scale up. • Realizing the full potential of digital innovations in Africa requires further development of these solutions and more nuanced evidence from both successful and unsuccessful scaling efforts. Despite enthusiasm for the potential of digital innovations to transform agricultural markets in Africa, progress made thus far has been limited to small-scale experiments that often fail to scale up. Realizing the full potential of digital innovations – tools, technologies, applications, and services – in Africa requires not just further development of these solutions at meaningful scales, but also more nuanced evidence from both successful and unsuccessful scaling efforts. This paper reviews the conceptual and empirical evidence on the transformative potential of digital innovations for African agricultural markets with an in-depth examination of solutions that have been rolled out to date in the continent. Specifically, the review addresses the following questions: (i) how can digital innovations improve the functioning of agricultural markets in Africa? (ii) what explains the apparent disconnect of most pilots to scale-ups? (iii) what is required to realize their full potential? and (iv) what are the emerging risks and opportunities associated with digital innovations for agricultural marketing? Although our review of the landscape and literature on market-focused digital innovations in Africa identifies several reasons to remain optimistic, the prevailing disconnect between pilots and scale-ups merits further evaluation. In particular, there is a need for more systematic assessments of both successes and failures at both the piloting and scaling stages. [ABSTRACT FROM AUTHOR]

This article uses nationally representative household-level panel data from Malawi to estimate how rural population density impacts agricultural intensification and household well-being. We find that areas of higher population density are associated with smaller farm sizes, lower real agricultural wage rates, and higher real maize prices. Any input intensification that occurs seems to be going to increasing maize yields, as we find no evidence that increases in population density enable farmers to increase gross value of crop output per hectare. We also find evidence that households in more densely populated areas increasingly rely on off-farm income to earn a living, but there appears to be a rural population density threshold beyond which households can no longer increase off-farm income per capita. [ABSTRACT FROM AUTHOR]

While much has been said in recent years about the importance of engaging rural youth in sub-Saharan Africa's development, the factual data about how African youth currently engage in rural economies remain sparse. We use recent nationally representative household survey data from six countries to describe the patterns of such engagement. We find that young people participate in agriculture at similar rates to older people. However, participation in non-farm wage employment and business activity changes with age, peaking in the 30s. The likelihood of reporting no activity is greatest for people in their 20s. In more remote places, people leave school earlier and are less likely to engage in the non-farm sector, compared with people in more accessible places. We also find evidence that the non-farm economy is more diversified in relatively more accessible places, offering a larger set of options for economic engagement. We show that 'landscapes of opportunity' , defined by economic remoteness and agricultural potential, are an important way of assessing the choices available to young rural people. A key conclusion is that efforts to develop a 'youth lens' for rural development should not abandon the mainstays of rural investment strategies such as infrastructure, education, and agricultural R&D. [ABSTRACT FROM AUTHOR]

Soil property and class maps for the continent of Africa were so far only available at very generalised scales, with many countries not mapped at all. Thanks to an increasing quantity and availability of soil samples collected at field point locations by various government and/or NGO funded projects, it is now possible to produce detailed pan-African maps of soil nutrients, including micro-nutrients at fine spatial resolutions. In this paper we describe production of a 30 m resolution Soil Information System of the African continent using, to date, the most comprehensive compilation of soil samples ( N ≈ 150 , 000 ) and Earth Observation data. We produced predictions for soil pH, organic carbon (C) and total nitrogen (N), total carbon, effective Cation Exchange Capacity (eCEC), extractable—phosphorus (P), potassium (K), calcium (Ca), magnesium (Mg), sulfur (S), sodium (Na), iron (Fe), zinc (Zn)—silt, clay and sand, stone content, bulk density and depth to bedrock, at three depths (0, 20 and 50 cm) and using 2-scale 3D Ensemble Machine Learning framework implemented in the mlr (Machine Learning in R) package. As covariate layers we used 250 m resolution (MODIS, PROBA-V and SM2RAIN products), and 30 m resolution (Sentinel-2, Landsat and DTM derivatives) images. Our fivefold spatial Cross-Validation results showed varying accuracy levels ranging from the best performing soil pH (CCC = 0.900) to more poorly predictable extractable phosphorus (CCC = 0.654) and sulphur (CCC = 0.708) and depth to bedrock. Sentinel-2 bands SWIR (B11, B12), NIR (B09, B8A), Landsat SWIR bands, and vertical depth derived from 30 m resolution DTM, were the overall most important 30 m resolution covariates. Climatic data images—SM2RAIN, bioclimatic variables and MODIS Land Surface Temperature—however, remained as the overall most important variables for predicting soil chemical variables at continental scale. This publicly available 30-m Soil Information System of Africa aims at supporting numerous applications, including soil and fertilizer policies and investments, agronomic advice to close yield gaps, environmental programs, or targeting of nutrition interventions. [ABSTRACT FROM AUTHOR]

• Site-specific nutrient management extension advice through digital support tool. • Three-year randomized control trial with 792 smallholder maize farmers in Nigeria. • Incremental increases (up to 18%) in nutrient application, yield and revenue. • Improved nutrient use efficiency and reduced greenhouse gas emission intensity. • Gradual and sustainable intensification of smallholder agriculture in Africa. There is growing evidence on the impacts of site-specific nutrient management (SSNM) from Asia. The evidence for Sub-Saharan Africa (SSA), where SSNM developments are more recent and where conditions concerning soil fertility and fertilizer use differ importantly from those in Asia, is extremely scarce. We evaluate a SSNM advisory tool that allows extension agents to generate fertilizer recommendations tailored to the specific situation of an individual farmer's field, using a three-year randomized controlled trial with 792 smallholder farmers in the maize belt of northern Nigeria. Two treatment arms were implemented: T1 and T2 both provide SSNM information on nutrient use and management, but T2 provides additional information on maize price distributions and the associated variability of expected returns to fertilizer use. We estimate average and heterogenous intent-to-treat effects on agronomic, economic and environmental plot-level outcomes. We find that T1 and T2 lead to substantial increases (up to 116%) in the adoption of good fertilizer management practices and T2 leads to incremental increases (up to 18%) in nutrient application rates, yields and revenues. Both treatments improve low levels of nutrient use efficiency and reduce high levels of greenhouse gas emission intensity, after two years of treatment. Our findings underscore the possibility of a more gradual and sustainable intensification of smallholder agriculture in SSA, as compared with the Asian Green Revolution, through increased fertilizer use accompanied by improved fertilizer management. [ABSTRACT FROM AUTHOR]

• This paper provides fresh evidence on the ex durante impacts of an armed conflict on households' food security in Ethiopia. • We also assess potential mechanisms and hence evaluate impacts on livelihood activities and access to food markets. • Seven months into the conflict, we find that the war increased the probability of moderate to severe food insecurity by 37 percentage points. • We show that exposure to one additional battle leads to 1 percentage point increase in the probability of moderate to severe food insecurity. • The conflict has significantly reduced access to food through supply chain disruptions and by curtailing non-farm livelihood activities. • Non-farm and wage related activities were affected the most whereas farming activities were relatively more resilient. Ethiopia recently experienced a large-scale war that lasted for more than two years. Using unique High-Frequency Phone Survey (HFPS) data, which span several months before and after the outbreak of the war, this paper provides evidence on the immediate impacts of the conflict on households' food security. We also assess potential mechanisms and evaluate impacts on proximate outcomes, including on livelihood activities and access to food markets. We use difference-in-differences and two-way fixed effects estimation to compare trends across affected and unaffected regions (households) and before and after the outbreak of the war. Seven months into the conflict, we find that the war was associated with a 37 percentage points increase in the probability of moderate to severe food insecurity. Using the Armed Conflict Location and Event Data (ACLED), we show that exposure to an additional battle leads to a 1 percentage point increase in the probability of moderate or severe food insecurity. The conflict was associated with significant reduction in access to food through supply chain disruptions and by curtailing non-farm livelihood activities. Non-farm and wage related activities were affected the most, whereas farming activities were relatively more resilient. Our estimates, which likely underestimate the true average effects on the population, constitute novel evidence on the near-real-time impacts of large-scale conflict. Our work highlights the potential of HFPS to monitor active and large-scale conflicts, especially in contexts where conventional data sources are not immediately available. [ABSTRACT FROM AUTHOR]

Abstract Innovations to improve staple crop germplasm can reduce poverty and otherwise improve farmer livelihoods through complex and multiple pathways. This paper reviews the evidence for one prominent pathway—through increased incomes (in cash and kind) for poor farmers who adopt the technology. An important determinant of poverty reduction is the ability of poor producers to adopt productivity-enhancing varieties, and the paper analyzes recent household-level data from two African countries to examine if poor producers face unique barriers to adoption. A second determinant of poverty reduction is the area available to plant these varieties and whether the intensity of adoption is great enough to significantly reduce poverty. The paper uses a double-hurdle estimation framework to model the adoption/area planted joint decision for maize farmers in Ethiopia and sweet potato farmers in Uganda. The focus of the analysis is the effect of poverty-related variables on adoption/area planted decisions. Farmer wealth, landholding, education, location, and access to support and information services are included to understand how correlates of poverty affect adoption decisions. We find evidence that landholding size is an important barrier to poverty reduction; poor farmers are able to adopt improved varieties, but their intensity is constrained by land availability. In Uganda, farmers at the 95th percentile of adoption area received about $0.13 per person per day from the incremental yield, covering < 50% of the mean household poverty gap. This gain only comes under optimistic assumptions and most adopters do not have sufficient area for the direct income effect to be large. The evidence suggests that direct, short-term impacts of increased productivity to increased income may be limited in magnitude. Nonetheless, we recognize that other, less direct pathways may be important, particularly over longer times. Impacts through indirect pathways are, however, more difficult to measure. This has implications for the design of M&E and the crafting of appropriate targets for outcomes of research on staple crops which should focus perhaps on the other pathways where poverty reduction is more probable. Highlights • Pathway from staple crop research to poverty reduction identified • Ability to adopt, intensity of adoption and productivity gains determine poverty reduction potential of new germplasm. • Poor farmers are generally able to adopt. • Intensity of adoption by poor farmers limits direct poverty-reducing effects. • Other pathways may be critical to poverty reduction effects of new germplasm. [ABSTRACT FROM AUTHOR]

Young people are increasingly linked to targeted agriculture and food security interventions. In Africa, the argument is that the combination of agricultural value chains, technology and entrepreneurship will unlock a sweet spot for youth employment. This article examines this argument from a rural transformations perspective. A framework is proposed with which to analyse young people's economic room to manoeuvre in different rural contexts and the differential abilities of young people to exploit associated opportunities. Using cereal agri-food systems as an example, the article identifies two new research areas that address important knowledge gaps: how young rural people in Africa engage with these systems and what pathways they use to become engaged. To address these questions, we propose an analytical framework built around key contextual factors that constrain or enable young people's economic activity. By pursuing the proposed research agenda, international agricultural research could make important contributions to both agricultural policy debates and development-oriented interventions. [ABSTRACT FROM AUTHOR]

• We use five rounds of panel data to examine rural households' resilience outcomes associated with participation in Ethiopia's Productive Safety Nets Program (PSNP). • We employ a probabilistic moment-based approach for measuring resilience and evaluate the role of PSNP transfers and duration of participation on households' resilience. • PSNP transfers below the median are less likely to generate meaningful improvements in resilience. • Continuous participation in the PSNP is associated with higher resilience. • Combining safety nets with income generating or asset building initiatives may be particularly efficacious at building poor households' resilience. • Boosting household resilience may require significant transfers over multiple years. Improving household resilience is becoming one of the key focus and target of social protection programs in Africa. However, there is surprisingly little direct evidence of the impacts of social protection programs on household resilience measures. We use five rounds of panel data to examine rural households' resilience outcomes associated with participation in Ethiopia's Productive Safety Nets Program (PSNP). Following Cissé and Barrett (2018), we employ a probabilistic moment-based approach for measuring resilience and evaluate the role of PSNP transfers and duration of participation on households' resilience. We document four important findings. First, although PSNP transfers are positively associated with resilience, PSNP transfers below the median are less likely to generate meaningful improvements in resilience. Second, continuous participation in the PSNP is associated with higher resilience. Third, combining safety nets with income generating or asset building initiatives may be particularly efficacious at building poor households' resilience. Fourth, our evaluation of both short-term welfare (consumption) and longer-term outcomes (resilience) suggests that these outcomes are likely to be driven by different factors, suggesting that optimizing intervention designs for improving short term welfare impacts may not necessarily improve households' resilience, and vice versa. Together, our findings imply that effectively boosting household resilience may require significant transfers over multiple years. National safety nets programs that transfer small amounts to beneficiaries over limited time horizons may not be very effective. [ABSTRACT FROM AUTHOR]

Recent global policy attention to "land grabs" by international investors, while very important, has diverted attention away from two other processes that may be even more fundamentally affecting Africa's economic development trajectory: (i) the pace of land acquisitions by medium-scale African investors, who now control more land than large-scale foreign investors in each of the three countries examined in this study (Ghana, Kenya, and Zambia); and (ii) the overall impact of land transactions on the viability of African governments' agricultural strategies, which for the most part remain predicated on smallholder-led development and will require the expansion of cropland by smallholder households. In Zambia and Ghana, the total farmland controlled by holdings between 5 and 100 hectares now exceeds the amount of land held by smallholder farms under 5 hectares. Farmland holdings in all three countries have become substantially more concentrated since the mid-1900s. The rapid rise in the number of farms in the 5 to 100 hectare category represents a relatively hidden but revolutionary change in farm structure, reflecting increased investment in land by relatively wealthy urban-based individuals. Only in Ghana has the rise of medium-scale farms been significantly associated with successful graduation of small-scale farmers into medium-scale stature. Continued rapid alienation of land to medium- and large-scale investors is likely to exacerbate localized land scarcity, restrict the potential of smallholder-led development, and put unrealistic pressure on the non-farm economy to absorb Africa's rapidly rising labor force. [ABSTRACT FROM AUTHOR]

• We examine the implication of the COVID-19 pandemic on household food security. • We difference-in-difference and exploit spatial variation in exposure to COVID-19. • Exposure to COVID-19 cases or lockdowns associated with increase in food insecurity. • The pandemic and lockdown associated with reduction in labor market participationThe pandemic and lockdown associated with reduction in labor market participation. • Lockdown related to 6–15 percentage points increase in food insecurity experience. This paper combines pre-pandemic face-to-face survey data with follow up phone surveys collected in April-May 2020 to examine the implication of the COVID-19 pandemic on household food security and labor market participation outcomes in Nigeria. To examine these relationships and implications, we exploit spatial variation in exposure to COVID-19 related infections and lockdown measures, along with temporal differences in our outcomes of interest, using a difference-in-difference approach. We find that households exposed to higher COVID-19 case rates or mobility lockdowns experience a significant increase in measures of food insecurity. Examining possible transmission channels for this effect, we find that the spread of the pandemic is associated with significant reductions in labor market participation. For instance, lockdown measures are associated with 6–15 percentage points increase in households' experience of food insecurity. Similarly, lockdown measures are associated with 12 percentage points reduction in the probability of participation in non-farm business activities. These lockdown measures have limited implications on wage-related activities and farming activities. In terms of food security, households relying on non-farm businesses, poorer households, and those living in remote and conflicted-affected zones have experienced relatively larger deteriorations in food security. These findings can help inform immediate and medium-term policy responses, including social protection policies aiming at ameliorating the impacts of the pandemic. [ABSTRACT FROM AUTHOR]