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Pearl millet [Pennisetum glaucum (L.) R. Br.] is a subtropical grain and forage crop. It is privileged with several desirable forage attributes. Nevertheless, research on pearl millet is limited, especially as a forage crop, in developing countries. Therefore, the objectives of this study were to investigate the field performance and stability of pearl millet genotypes for forage yield across seven environments. The study was conducted in seven environments (combination of locations and seasons) during the 2016/2017–2018/2019 seasons. Twenty-five pearl millet genotypes, selected based on forage yield from a core collection of 200 accessions, were arranged in an alpha lattice design with three replications. The parameters measured were fresh forage yield, days to flowering, plant height, number of culms m−2, leaf-to-stem ratio, and stem girth. The combined analysis revealed that environments, genotypes, and their interaction had significant effects on all traits studied except the genotypic effect on stem girth. Across the seven environments, four genotypes (G14, G01, G12, and G22) outyielded the check genotype in fresh matter yield by 20.7, 16.5, 11.0 and 9.8%, respectively. The additive main effects and multiplicative interaction (AMMI) analysis showed that the genotype, environment, and their interaction were highly significant (p ≤ 0.001) for fresh matter yield. The results of AMMI stability values (ASVs) and the genotype selection index (GSI) combined with the AMMI estimate-based selection showed that genotypes G14, G22 and G01 were the most stable and adapted genotypes and were superior to the check genotype. These results indicate that forage pearl millet varieties could be developed directly through evaluating the wealth of available collections or indirectly through hybridization in crop breeding programs.

Developing climate-resilient crop varieties with better performance under variable environments is essential to ensure food security in a changing climate. This process is significantly influenced, among other factors, by genotype × environment (G × E) interactions. With the objective of identifying high-yielding and stable genotypes, 20 elite durum wheat lines were evaluated in 24 environments (location–season combination) during 5 crop seasons (2010/11–2014/15). The REML (residual maximum likelihood)-predicted means of grain yield of 16 genotypes that were common across all environments ranged from 3522 kg/ha in G201 to 4132 kg/ha in G217. Results of additive main effect and multiplicative interaction (AMMI) analysis showed that genotypes (G), environments (E), and genotype × environment interaction (GEI) significantly affected grain yield. From the total sum of squares due to treatments (G + E + GEI), E attributed the highest proportion of the variation (90.0%), followed by GEI (8.7%) and G (1.3%). Based on the first four AMMI selections for grain yield in the 24 environments, genotypes G217, G219, G211, and G213 were selected in 23, 12, 11, and 9 environments, respectively. The genotype and genotype × environment biplot (GGE) biplot polygon view showed that the environments were separated into three mega-environments. The winning genotypes in these mega-environments were G217, G214, and G204. Genotypes G212, G220, G217, G215, and G213 showed low AMMI stability values (ASV), whereas genotypes G217, G220, G212, G211, and G219 showed low genotype selection index (GSI), indicating their better stability and adaptability to the test environments. The results indicated that genotypes G217, G219, G211, G213, and G220 combined both high grain yield and stability/adaptability under dry but irrigated and heat-prone environments. An in-depth analysis of the superior genotypes could help better understand the stress-adaptive traits that could be targeted to further increase durum wheat yield and stability under the changing climate.

The innovation platforms (IP) concept was adopted in Sudan as an effective multi-stakeholders approach for sustainable agricultural R4D. The objectives of this study were to evaluate the IP approach in enhancing wheat production and contributing to food security in Sudan. Six community-based IPs were established in four states. A range of activities were implemented within IPs including technology demonstration, seed production, capacity strengthening, field days, etc. A total of 440 farmer-managed demonstration plots were conducted and yields up to 6.0–8.0 t/ha achieved in some fields. The 4-year average yield across all IPs was 3.8 t/ha compared to the national average of 2.3 t/ha. A total of 2031 (66.6% female) trainees participated in 31 training courses targeted all stakeholders along the value chain. A total of 4335 tons of seeds produced and distributed to seed producers and farmers. More than 2500 tons of seeds produced by farmers diffused to neighbouring farmers. Due to the raised awareness of improved varieties, demand for quality seeds increased. Overall, more than 15,700 (31% female) stakeholders were directly reached. Coupled with enabling production measures, improved technologies encouraged national wheat production and productivity to significantly increase since 2011/2012 by 118,000 ton/year and 0.39 ton/ha/year, respectively. The IP approach was able to engage all stakeholders and brought changes in the attitude of many of them. The IP concept was adopted for technology dissemination and enhancing interaction among stakeholders of a number of crops in Sudan.

The Global Wheat Head Detection (GWHD) dataset was created in 2020 and has assembled 193,634 labelled wheat heads from 4700 RGB images acquired from various acquisition platforms and 7 countries/institutions. With an associated competition hosted in Kaggle, GWHD_2020 has successfully attracted attention from both the computer vision and agricultural science communities. From this first experience, a few avenues for improvements have been identified regarding data size, head diversity, and label reliability. To address these issues, the 2020 dataset has been reexamined, relabeled, and complemented by adding 1722 images from 5 additional countries, allowing for 81,553 additional wheat heads. We now release in 2021 a new version of the Global Wheat Head Detection dataset, which is bigger, more diverse, and less noisy than the GWHD_2020 version.

Heat stress is one of the major environmental constraints for wheat production; thus, a comprehensive understanding of the metabolomic responses of wheat is required for breeding heat-tolerant varieties. In this study, the metabolome responses of heat-tolerant genotypes Imam and Norin 61, and susceptible genotype Chinese Spring were comparatively analyzed using Fourier transform infrared (FTIR) spectroscopy in combination with chemometric data mining techniques. Principal component analysis of the FTIR data suggested a spectral feature partially overlapping between the three genotypes. FTIR spectral biomarker assay showed similar heat responses between the genotypes for markers Fm482 and Fm1502, whereas genotype-dependent variations were observed for other markers. The markers Fm1251 and Fm1729 showed contrasting behaviors between heat-tolerant and susceptible genotypes, suggesting that these markers may potentially serve as a tool for distinguishing heat-tolerant genotypes. Linear discriminant analysis (LDA) of the spectra demonstrated a clear separation between the three genotypes in terms of the heat stress effect. Analysis of LDA coefficients identified several spectral regions that were potentially responsible for the discrimination of FTIR spectra between different genotypes and environments. These results suggest that a combination of FTIR and chemometrics can be a useful technique for characterizing the metabolic behavior of diverse wheat genotypes under heat stress.

Drought and high temperature are the major abiotic stresses for wheat production. The present study investigated the effect of drought and chronic heat stress on physiological parameters of durum wheat lines derived from interspecific crosses and their association with yield. Seventy-seven durum wheat lines were evaluated during two seasons (2016–2017 and 2017–2018) for drought tolerance at Tessaout (Morocco) using irrigated and rainfed treatments and for heat tolerance at Wad Medani (Sudan). Five drought screening indices (alone or combined) and physiological parameters were used to assess drought and heat tolerance. Among the physiological parameters used, canopy temperature (CT) had moderate heritability and was significantly affected by both severe and moderate drought stresses. CT at early heading showed a stronger correlation with grain yield (GY) and total biomass (BY) under heat stress. The use of maximum quantum yield of PSII (Fv/Fm) for drought/heat screening was limited by the low genetic variation despite its significant correlation with yield under drought (r2 = 0.22) and heat (r2 = 0.4). The normalized difference vegetation index (NDVI) at vegetative stage was highly correlated with GY and BY and it showed high genotypic variation that can allow for efficient selection. The grain filling rate (GFR) was found to be highly correlated with GY and BY under heat stress. The modified stress tolerance index (MsSTI) had the highest association with GY under drought (R2 = 0.82) while the mean productivity (MP) was adapted to both optimal conditions (R2 = 0.77) and drought stress (R2 = 0.73). The computation of a mean score index (MSI) improved the selection efficiency under drought (R2 = 0.92). The results showed good potential for lines derived from wide crosses to increase variability for heat and drought adaptive physiological traits.

The boom of the 4.0 industrial revolution and the Covid-19 pandemic have changed the teaching and learning process, where digital learning environments have become increasingly necessary and convenient. The application of game-based learning (GBL) provides many benefits, such as helping to improve the quality of the mathematics teaching and learning process. This paper describes digital GBL with the application of Learning Analytics (LA) in a primary school and how the LA approach can enhance learning with GBL. GBL allows students to have more practice in their learning, while LA allows the data on teaching and learning materials to be recorded. The recorded data allows the teachers to systematically analyze them to keep track of students' learning performance. The use of LA is also useful to researchers and game designers. This paper has also proven the importance of the cognitive load theory and its application in GBL. It allows the use of GBL-based application to present learning information to cater to students' cognitive needs. This study involved sixty-four students from a rural primary school who participated in a 10-week GBL-based intervention. The results show that digital GBL helped increase the students' achievement in mathematics. Moreover, the LA process could be used as a mathematics achievement prediction model. In this regard, data on login frequency, duration of use, and the score to predict could be used to predict the intervention's impact on mathematics achievements. The study concludes by discussing the findings and their implications towards GBL and LA-based research and practice in mathematics teaching and learning.

Developing climate-resilient crop varieties with better performance under variable environments is essential to ensure food security in a changing climate. This process is significantly influenced, among other factors, by genotype × environment (G × E) interactions. With the objective of identifying high-yielding and stable genotypes, 20 elite durum wheat lines were evaluated in 24 environments (location–season combination) during 5 crop seasons (2010/11–2014/15). The REML (residual maximum likelihood)-predicted means of grain yield of 16 genotypes that were common across all environments ranged from 3522 kg/ha in G201 to 4132 kg/ha in G217. Results of additive main effect and multiplicative interaction (AMMI) analysis showed that genotypes (G), environments (E), and genotype × environment interaction (GEI) significantly affected grain yield. From the total sum of squares due to treatments (G + E + GEI), E attributed the highest proportion of the variation (90.0%), followed by GEI (8.7%) and G (1.3%). Based on the first four AMMI selections for grain yield in the 24 environments, genotypes G217, G219, G211, and G213 were selected in 23, 12, 11, and 9 environments, respectively. The genotype and genotype × environment biplot (GGE) biplot polygon view showed that the environments were separated into three mega-environments. The winning genotypes in these mega-environments were G217, G214, and G204. Genotypes G212, G220, G217, G215, and G213 showed low AMMI stability values (ASV), whereas genotypes G217, G220, G212, G211, and G219 showed low genotype selection index (GSI), indicating their better stability and adaptability to the test environments. The results indicated that genotypes G217, G219, G211, G213, and G220 combined both high grain yield and stability/adaptability under dry but irrigated and heat-prone environments. An in-depth analysis of the superior genotypes could help better understand the stress-adaptive traits that could be targeted to further increase durum wheat yield and stability under the changing climate. [ABSTRACT FROM AUTHOR]

High temperatures pose a major threat to wheat productivity and necessitate the development of new cultivars that are resilient to future heat stress. Wild emmer (Triticum turgidum L. ssp. dicoccoides), which is a direct progenitor of domesticated durum wheat (Triticum turgidum L. ssp. durum) and contributor to the A and B genome of bread wheat (Triticum aestivum), offers a valuable genetic reservoir for developing climate-resilient wheat. However, the morphology of wild emmer is different from that of durum and bread wheat, in particular, the spikelets are fragile and naturally fall off, making it difficult to study its agronomic traits. In this study, we created nine backcrossed families between the popular durum wheat cultivar ‘Miki 3’ and nine wild emmer accessions collected from northern and southern lineages of this species. The objective was to investigate the intraspecific genetic variation in wild emmer and identify traits associated with heat stress tolerance. We evaluated these nine families under multi-environments ranging from optimum to severe heat stress conditions in Japan and Sudan and measured important agronomic traits. The result showed that two families, developed from accessions of both northern and southern lineages exhibited high harvest index, elevated chlorophyll content, and reduced canopy temperature under heat stress. Additionally, one family developed from an accession of the southern lineage displayed high biomass, harvest index, and seed number under heat-stress conditions. These three families produced high heat tolerant lines with unique introgressed segments from their wild emmer parents on chromosomes 1A, 2B, 5B, 6B, and 7B, which may be linked to heat resilience. From these results, we were able to identify significant intraspecific diversity between the wild emmer accessions in terms of heat stress tolerance. However, no significant tendency between the northern and southern lineages of wild emmer has been identified. These findings emphasize the need to harness not only the interspecific but also the intraspecific genetic variation of wild emmer diversity to uncover valuable genes for heat stress tolerance in wheat breeding programs.

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The boom of the 4.0 industrial revolution and the Covid-19 pandemic have changed the teaching and learning process, where digital learning environments have become increasingly necessary and convenient. The application of game-based learning (GBL) provides many benefits, such as helping to improve the quality of the mathematics teaching and learning process. This paper describes digital GBL with the application of Learning Analytics (LA) in a primary school and how the LA approach can enhance learning with GBL. GBL allows students to have more practice in their learning, while LA allows the data on teaching and learning materials to be recorded. The recorded data allows the teachers to systematically analyze them to keep track of students' learning performance. The use of LA is also useful to researchers and game designers. This paper has also proven the importance of the cognitive load theory and its application in GBL. It allows the use of GBL-based application to present learning information to cater to students' cognitive needs. This study involved sixty-four students from a rural primary school who participated in a 10-week GBL-based intervention. The results show that digital GBL helped increase the students' achievement in mathematics. Moreover, the LA process could be used as a mathematics achievement prediction model. In this regard, data on login frequency, duration of use, and the score to predict could be used to predict the intervention's impact on mathematics achievements. The study concludes by discussing the findings and their implications towards GBL and LA-based research and practice in mathematics teaching and learning. [ABSTRACT FROM AUTHOR]

Sheep husbandry is considered one of the most important activities in the socio-economic development in the Middle East region, especially in Iraq and Islamic Republic of Iran (IRI). Therefore this study was designed to evaluate the level of ovine pasteurellosis vaccine protective antibody titer and identification of the prevailing serotypes in Iraq (Basrah, Baghdad, Tikrit, Mosul, Erbil). The vaccine was made from pasteurella multosida Bio-type A and the serotypes of Mannheimia haemolytica. This investigation was performed from September 2021 to January 2022, in Iraq. Sheep blood sera samples were obtained from control unvaccinated and vaccinated sheep after 14, 21 and 28 days post vaccination. The results showed that out of 319 sheep blood sera samples which were evaluated using indirect Haemagglutination (IHA) test to detect Mannheimia haemolytica serotypes, the high prevalence (100 %) of M. haemolytica A2 was found in all the five study regions area, while 96.5 % was M. haemolytica A7 and 88.1 % was M. haemolytica A1. The level of antibody titer was measured by specific serum antibody titer of pasteurella multosida Bio-type A. The results revealed that out of 268 vaccinated blood sera samples the overall antibody titer were 12 (3.8 %), 16 (5%) and 17 (5.3 %) for protective antibody titer of 1:160, 1:80 and 1:40 respectively and for antibody titer of 1:20 were 15 (4.7%) and for antibody titer of 1:10 were 17 (5.3 %), whereas the antibody titer in the control group was 4 (7.8 %). The result of this study indicated that the vaccine administered has limited protective power against pasteurella multocida Bio-type A which lead to researchers for further study on identification of specific strain of pasteurella multosida and development of multivalent vaccine including the most prevalent pasteurella serotypes. [ABSTRACT FROM AUTHOR]