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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]

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]

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]

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]

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]

• 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 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]

• 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]

• 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]

• 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]