Phenotyping conservation agriculture management effects on ground and aerial remote sensing assessments of maize hybrids performance in Zimbabwe

n the coming decades, Sub-Saharan Africa (SSA) faces challenges to sustainably increasefood production while keeping pace with continued population growth. Conservation agriculture(CA) has been proposed to enhance soil health and productivity to respond to this situation.Maize is the main staple food in SSA. To increase maize yields, the selection of suitable genotypes andmanagement practices for CA conditions has been explored using remote sensing tools. They may playa fundamental role towards overcoming the traditional limitations of data collection and processing inlarge scale phenotyping studies. We present the result of a study in which Red-Green-Blue (RGB) andmultispectral indexes were evaluated for assessing maize performance under conventional ploughing(CP) and CA practices. Eight hybrids under different planting densities and tillage practices weretested. The measurements were conducted on seedlings at ground level (0.8 m) and from an unmannedaerial vehicle (UAV) platform (30 m), causing a platform proximity effect on the images resolution thatdid not have any negative impact on the performance of the indexes. Most of the calculated indexes(Green Area (GA) and Normalized Difference Vegetation Index (NDVI)) were significantly affectedby tillage conditions increasing their values from CP to CA. Indexes derived from the RGB-imagesrelated to canopy greenness performed better at assessing yield differences, potentially due to thegreater resolution of the RGB compared with the multispectral data, although this performance wasmore precise for CP than CA.The correlations of the multispectral indexes with yield were improvedby applying a soil-mask derived from a NDVI threshold with the aim of corresponding pixels withvegetation. The results of this study highlight the applicability of remote sensing approaches basedon RGB images to the assessment of crop performance and hybrid choice

Effectiveness of a Community-Based Intervention to Increase Supermarket Vendors' Compliance with Age Restrictions for Alcohol Sales in Spain: A Pilot Study.

In Spain the legal age to buy alcohol is 18 years. However, official surveys show that minors perceive alcohol availability to be easy. This paper describes the impacts of a community-based intervention to increase vendors' compliance with age limits regarding alcohol sales in supermarkets. The aim of this study was to explore the association between implementation of a multicomponent intervention to reduce adolescents' alcohol use and sale of alcohol to minors in the city of Palma (Spain). Twenty trained adolescents (14-17 years old) conducted 138 alcohol test purchases in nine supermarket chains in August 2018 (baseline; n = 73) prior to the intervention, and again in January 2020 (follow-up; n = 65). Analysis was conducted according to three levels of intervention implemented across the supermarkets: (i) personnel from the supermarkets' Human Resources or Corporate Social Responsibility teams received alcohol service training as trainers (i.e., community mobilization); (ii) managers and vendors training by the capacitated trainers; and (iii) no training of managers or vendors (i.e., control group). In the supermarkets that completed the Training of Trainers and the vendors' training program, average sales decreased significantly from 76.9% in 2018 to 45.5% in 2020, asking for the age of the shopper significantly increased from 3.8% to 45.4%, and asking for proof of age significantly increased from 15.4% to 72.7%. Additionally, a statistically significant increase was observed in the visibility of prohibition to sell alcohol to minors' signs, from 61.5% to 100%. No statistically significant differences were found for the Training of Trainers intervention alone nor in the control group. In conclusion, community mobilization combined with staff training is associated with significant increases in supermarket vendors' compliance with alcohol legislation in Spain

Evaluating Maize Genotype Performance under Low Nitrogen Conditions Using RGB UAV Phenotyping Techniques

Maize is the most cultivated cereal in Africa in terms of land area and production, but low soil nitrogen availability often constrains yields. Developing new maize varieties with high and reliable yields using traditional crop breeding techniques in field conditions can be slow and costly. Remote sensing has become an important tool in the modernization of field-based high-throughput plant phenotyping (HTPP), providing faster gains towards the improvement of yield potential and adaptation to abiotic and biotic limiting conditions. We evaluated the performance of a set of remote sensing indices derived from red-green-blue (RGB) images along with field-based multispectral normalized difference vegetation index (NDVI) and leaf chlorophyll content (SPAD values) as phenotypic traits for assessing maize performance under managed low-nitrogen conditions. HTPP measurements were conducted from the ground and from an unmanned aerial vehicle (UAV). For the ground-level RGB indices, the strongest correlations to yield were observed with hue, greener green area (GGA), and a newly developed RGB HTPP index, NDLab (normalized difference Commission Internationale de I´Edairage (CIE)Lab index), while GGA and crop senescence index (CSI) correlated better with grain yield from the UAV. Regarding ground sensors, SPAD exhibited the closest correlation with grain yield, notably increasing in its correlation when measured in the vegetative stage. Additionally, we evaluated how different HTPP indices contributed to the explanation of yield in combination with agronomic data, such as anthesis silking interval (ASI), anthesis date (AD), and plant height (PH). Multivariate regression models, including RGB indices (R2 > 0.60), outperformed other models using only agronomic parameters or field sensors (R2 > 0.50), reinforcing RGB HTPP's potential to improve yield assessments. Finally, we compared the low-N results to the same panel of 64 maize genotypes grown under optimal conditions, noting that only 11% of the total genotypes appeared in the highest yield producing quartile for both trials. Furthermore, we calculated the grain yield loss index (GYLI) for each genotype, which showed a large range of variability, suggesting that low-N performance is not necessarily exclusive of high productivity in optimal conditions

Leaf versus whole-canopy remote sensing methodologies for crop monitoring under conservation agriculture: a case of study with maize in Zimbabwe

Enhancing nitrogen fertilization efficiency for improving yield is a major challenge for smallholder farming systems. Rapid and cost-effective methodologies with the capability to assess the effects of fertilization are required to facilitate smallholder farm management. This study compares maize leaf and canopy-based approaches for assessing N fertilization performance under different tillage, residue coverage and top-dressing conditions in Zimbabwe. Among the measurements made on individual leaves, chlorophyll readings were the best indicators for both N content in leaves (R < 0.700) and grain yield (GY) (R < 0.800). Canopy indices reported even higher correlation coefficients when assessing GY, especially those based on the measurements of the vegetation density as the green area indices (R < 0.850). Canopy measurements from both ground and aerial platforms performed very similar, but indices assessed from the UAV performed best in capturing the most relevant information from the whole plot and correlations with GY and leaf N content were slightly higher. Leaf-based measurements demonstrated utility in monitoring N leaf content, though canopy measurements outperformed the leaf readings in assessing GY parameters, while providing the additional value derived from the affordability and easiness of using a pheno-pole system or the high-throughput capacities of the UAVs

Evaluating maize genotype performance under low nitrogen conditions using RGB UAV phenotyping techniques

Maize is the most cultivated cereal in Africa in terms of land area and production, but low soil nitrogen availability often constrains yields. Developing new maize varieties with high and reliable yields using traditional crop breeding techniques in field conditions can be slow and costly. Remote sensing has become an important tool in the modernization of field-based high-throughput plant phenotyping (HTPP), providing faster gains towards the improvement of yield potential and adaptation to abiotic and biotic limiting conditions. We evaluated the performance of a set of remote sensing indices derived from red–green–blue (RGB) images along with field-based multispectral normalized difference vegetation index (NDVI) and leaf chlorophyll content (SPAD values) as phenotypic traits for assessing maize performance under managed low-nitrogen conditions. HTPP measurements were conducted from the ground and from an unmanned aerial vehicle (UAV). For the ground-level RGB indices, the strongest correlations to yield were observed with hue, greener green area (GGA), and a newly developed RGB HTPP index, NDLab (normalized difference Commission Internationale de I´Edairage (CIE)Lab index), while GGA and crop senescence index (CSI) correlated better with grain yield from the UAV. Regarding ground sensors, SPAD exhibited the closest correlation with grain yield, notably increasing in its correlation when measured in the vegetative stage. Additionally, we evaluated how different HTPP indices contributed to the explanation of yield in combination with agronomic data, such as anthesis silking interval (ASI), anthesis date (AD), and plant height (PH). Multivariate regression models, including RGB indices (R2 > 0.60), outperformed other models using only agronomic parameters or field sensors (R2 > 0.50), reinforcing RGB HTPP’s potential to improve yield assessments. Finally, we compared the low-N results to the same panel of 64 maize genotypes grown under optimal conditions, noting that only 11% of the total genotypes appeared in the highest yield producing quartile for both trials. Furthermore, we calculated the grain yield loss index (GYLI) for each genotype, which showed a large range of variability, suggesting that low-N performance is not necessarily exclusive of high productivity in optimal conditions.This research and APC was funded by Bill & Melinda Gates Foundation and USAID Stress Tolerant Maize for Africa program, grant number [OPP1134248], and the MAIZE CGIAR research program. The CGIAR Research Program MAIZE receives W1&W2 support from the Governments of Australia, Belgium, Canada, China, France, India, Japan, Korea, Mexico, Netherlands, New Zealand, Norway, Sweden, Switzerland, U.K., U.S., and the World Bank

Comparative performance of ground vs. aerially assessed RGB and multispectral indices for early-growth evaluation of maize performance under phosphorus fertilization

Low soil fertility is one of the factors most limiting agricultural production, with phosphorus deficiency being among the main factors, particularly in developing countries. To deal with such environmental constraints, remote sensing measurements can be used to rapidly assess crop performance and to phenotype a large number of plots in a rapid and cost-effective way. We evaluated the performance of a set of remote sensing indices derived from Red-Green-Blue (RGB) images and multispectral (visible and infrared) data as phenotypic traits and crop monitoring tools for early assessment of maize performance under phosphorus fertilization. Thus, a set of 26 maize hybrids grown under field conditions in Zimbabwe was assayed under contrasting phosphorus fertilization conditions. Remote sensing measurements were conducted in seedlings at two different levels: at the ground and from an aerial platform. Within a particular phosphorus level, some of the RGB indices strongly correlated with grain yield. In general, RGB indices assessed at both ground and aerial levels correlated in a comparable way with grain yield except for indices a* and u*, which correlated better when assessed at the aerial level than at ground level and Greener Area (GGA) which had the opposite correlation. The Normalized Difference Vegetation Index (NDVI) evaluated at ground level with an active sensor also correlated better with grain yield than the NDVI derived from the multispectral camera mounted in the aerial platform. Other multispectral indices like the Soil Adjusted Vegetation Index (SAVI) performed very similarly to NDVI assessed at the aerial level but overall, they correlated in a weaker manner with grain yield than the best RGB indices. This study clearly illustrates the advantage of RGB-derived indices over the more costly and time-consuming multispectral indices. Moreover, the indices best correlated with GY were in general those best correlated with leaf phosphorous content. However, these correlations were clearly weaker than against grain yield and only under low phosphorous conditions. This work reinforces the effectiveness of canopy remote sensing for plant phenotyping and crop management of maize under different phosphorus nutrient conditions and suggests that the RGB indices are the best option

Comparative UAV and field phenotyping to assess yield and nitrogen use efficiency in hibrid and conventional barley

With the commercialization and increasing availability of Unmanned Aerial Vehicles (UAVs) multiple rotor copters have expanded rapidly in plant phenotyping studies with their ability to provide clear, high resolution images. As such, the traditional bottleneck of plant phenotyping has shifted from data collection to data processing. Fortunately, the necessarily controlled and repetitive design of plant phenotyping allows for the development of semi-automatic computer processing tools that may sufficiently reduce the time spent in data extraction. Here we present a comparison of UAV and field based high throughput plant phenotyping (HTPP) using the free, open-source image analysis software FIJI (Fiji is just ImageJ) using RGB (conventional digital cameras), multispectral and thermal aerial imagery in combination with a matching suite of ground sensors in a study of two hybrids and one conventional barely variety with ten different nitrogen treatments, combining different fertilization levels and application schedules. A detailed correlation network for physiological traits and exploration of the data comparing between treatments and varieties provided insights into crop performance under different management scenarios. Multivariate regression models explained 77.8, 71.6, and 82.7% of the variance in yield from aerial, ground, and combined data sets, respectively

Hemoglobin switching in sheep and goats: induction of hemoglobin C synthesis in cultures of sheep fetal erythroid cells.

Synthesis of HbF (alpha2gamma2) is replaced by synthesis of Hb A (alpha2betaA2) shortly before birth in sheep homozygous for the betaA globin chain whereas Hb C (alpha2betaC2) is produced transiently during the neonatal period. We have obtained a Hb F-to-Hb C switch by generating erythroid colonies at high erythropoietin concentration in plasma clot cultures of mid-gestation fetal bone marrow or liver. Furthermore, high erythropoietin concentration appeared specifically to activate the gene for betaC and not those for betaA or betaB globin in colonies grown from cells of an animal heterozygous for the betaA and betaB genes. Erythropoietic stress in the form of periodic bleeding or erythropoietin injection in utero did not stimulate production of Hb C (alpha2betaC2) in fetal sheep until shortly before birth, and then only in two of six animals. Thus, factors other than erythropoietin may influence the potential for betaC globin synthesis in vivo in fetal sheep