Temporal rainfall trend analysis in different agro-ecological regions of southern Africa

Rainfall is a major driver of food production in rainfed smallholder farming systems. This study was conducted to assess linear trends in (i) different daily rainfall amounts (<5, 5–10, 11–20, 21–40 and >40 mm∙day-1), and (ii) monthly and seasonal rainfall amounts. Drought was determined using the rainfall variability index. Daily rainfall data were derived from 18 meteorological stations in southern Africa. Daily rainfall was dominated by <5 mm∙day-1 followed by 5–10 mm∙day -1. Three locations experienced increasing linear trends of <5 mm∙day-1 amounts and two others in sub-humid region had increases in the >40 mm day -1 category. Semi-arid location experienced increasing trends in <5 and 5–10 mm∙day-1 events. A significant linear trend in seasonal rainfall occurred at two locations with decreasing rainfall (1.24 and 3 mm∙season-1). A 3 mm∙season-1 decrease in seasonal rainfall was experienced under semi-arid conditions. There were no apparent linear trends in monthly and seasonal rainfall at 15 of the 18 locations studied. Drought frequencies varied with location and were 50% or higher during the November–March growing season. Rainfall trends were location and agro-ecology specific, but most of the locations studied did not experience significant changes between the 1900s and 2000s

Temporal rainfall trend analysis in different agro-ecological regions of southern Africa

Open Access ArticleRainfall is a major driver of food production in rainfed smallholder farming systems. This study was conducted to assess linear trends in (i) different daily rainfall amounts (40 mm∙day-1), and (ii) monthly and seasonal rainfall amounts. Drought was determined using the rainfall variability index. Daily rainfall data were derived from 18 meteorological stations in southern Africa. Daily rainfall was dominated by 40 mm day -1 category. Semi-arid location experienced increasing trends in <5 and 5–10 mm∙day-1 events. A significant linear trend in seasonal rainfall occurred at two locations with decreasing rainfall (1.24 and 3 mm∙season-1). A 3 mm∙season-1 decrease in seasonal rainfall was experienced under semi-arid conditions. There were no apparent linear trends in monthly and seasonal rainfall at 15 of the 18 locations studied. Drought frequencies varied with location and were 50% or higher during the November–March growing season. Rainfall trends were location and agro-ecology specific, but most of the locations studied did not experience significant changes between the 1900s and 2000s

Temporal rainfall trend analysis in different agro-ecological regions of southern Africa

Rainfall is a major driver of food production in rainfed smallholder farming systems. This study was conducted to assess linear trends in (i) different daily rainfall amounts (&lt;5, 5–10, 11–20, 21–40 and &gt;40 mm∙day-1), and (ii) monthly and seasonal rainfall amounts. Drought was determined using the rainfall variability index. Daily rainfall data were derived from 18 meteorological stations in southern Africa. Daily rainfall was dominated by &lt;5 mm∙day-1 followed by 5–10 mm∙day-1. Three locations experienced increasing linear trends of &lt;5 mm∙day-1 amounts and two others in sub-humid region had increases in the &gt;40 mm day-1 category. Semi-arid location experienced increasing trends in &lt;5 and 5–10 mm∙day-1 events. A significant linear trend in seasonal rainfall occurred at two locations with decreasing rainfall (1.24 and 3 mm∙season-1). A 3 mm∙season-1 decrease in seasonal rainfall was experienced under semi-arid conditions. There were no apparent linear trends in monthly and seasonal rainfall at 15 of the 18 locations studied. Drought frequencies varied with location and were 50% or higher during the November–March growing season. Rainfall trends were location and agro-ecology specific, but most of the locations studied did not experience significant changes between the 1900s and 2000s

Correction to: High-throughput method for ear phenotyping and kernel weight estimation in maize using ear digital imaging

[This corrects the article DOI: 10.1186/s13007-018-0317-4.]

High-throughput method for ear phenotyping and kernel weight estimation in maize using ear digital imaging

BackgroundGrain yield, ear and kernel attributes can assist to understand the performance of maize plant under different environmental conditions and can be used in the variety development process to address farmer's preferences. These parameters are however still laborious and expensive to measure.ResultsA low-cost ear digital imaging method was developed that provides estimates of ear and kernel attributes i.e., ear number and size, kernel number and size as well as kernel weight from photos of ears harvested from field trial plots. The image processing method uses a script that runs in a batch mode on ImageJ; an open source software. Kernel weight was estimated using the total kernel number derived from the number of kernels visible on the image and the average kernel size. Data showed a good agreement in terms of accuracy and precision between ground truth measurements and data generated through image processing. Broad-sense heritability of the estimated parameters was in the range or higher than that for measured grain weight. Limitation of the method for kernel weight estimation is discussed.ConclusionThe method developed in this work provides an opportunity to significantly reduce the cost of selection in the breeding process, especially for resource constrained crop improvement programs and can be used to learn more about the genetic bases of grain yield determinants

High-throughput method for ear phenotyping and kernel weight estimation in maize using ear digital imaging (vol 14, pg 49, 2018)

After the publication of our article [1], it was brought to our attention that in six places in the article we omitted to use quotation marks to show where the text has been directly used from the cited references

High-throughput method for ear phenotyping and kernel weight estimation in maize using ear digital imaging

Abstract Background Grain yield, ear and kernel attributes can assist to understand the performance of maize plant under different environmental conditions and can be used in the variety development process to address farmer’s preferences. These parameters are however still laborious and expensive to measure. Results A low-cost ear digital imaging method was developed that provides estimates of ear and kernel attributes i.e., ear number and size, kernel number and size as well as kernel weight from photos of ears harvested from field trial plots. The image processing method uses a script that runs in a batch mode on ImageJ; an open source software. Kernel weight was estimated using the total kernel number derived from the number of kernels visible on the image and the average kernel size. Data showed a good agreement in terms of accuracy and precision between ground truth measurements and data generated through image processing. Broad-sense heritability of the estimated parameters was in the range or higher than that for measured grain weight. Limitation of the method for kernel weight estimation is discussed. Conclusion The method developed in this work provides an opportunity to significantly reduce the cost of selection in the breeding process, especially for resource constrained crop improvement programs and can be used to learn more about the genetic bases of grain yield determinants

Voluntary counseling and testing by nurse counselors: what is the role of routine repeated testing after a negative result?

Three hundred eighty-eight human immunodeficiency virus (HIV)-negative clients in Zimbabwe were retested at 3 months using 2 parallel rapid tests. One operator error (risk, 0.26%; 95% confidence interval, 0.0065%-1.4%) and no "true" seroconversions (upper 95% confidence limit, 0.96%) were detected. High-risk behavior was not significantly reduced. Policies recommending routine retesting need to be reconsidered