Ministry of Agriculture & Farmers Welfare

Abstract A laboratory and warehouse study was conducted during the 2024/2025 season in the laboratories and warehouses of the Plant Protection Department of the College of Agriculture, Tikrit University/Iraq. The study included the use of silver nanoparticles and biosynthetic zinc nanoparticles from the food mushroom Pleurotus ostreatus A2019 for four parts: (mushroom filtrate, biomass, cold extract and Hot extract) to preserve the safety and health of seeds from black point disease for six varieties of Iraqi wheat. The results of the study showed that the two fungi, Altrnaria spp and Fusarium spp, had the highest appearance rate among the isolated and diagnosed fungi, as the percentage of the two fungi reached 17.50% and 19.25%, respectively. The experimental treatment was reduced to study the effect Concentrations of silver nanoparticles and zinc nanoparticles on the fungi Alternaria spp and Fusarium spp, which cause black spot disease (cm). Treating the fungus filtrate with silver nanoparticles (AgNPs) at a concentration of 2 mm gave the highest rate of inhibition, reaching 0·640 cm for the fungus Alternaria spp and 0·520 cm for the fungus Fusarium spp at the same concentration and the same treatment, while the highest percentage was Germination before storage for the Iraq 2 variety reached 92.25%, while the highest germination rate after storage was for the Aba variety, which reached 97.75%. As for estimating the storage infection rate based on the pathogenic fungus Fusarium spp, the lowest infection rate was in the treatment consisting of (AgNPs) + pesticide + fungus, as it reached 0.79% and 0.75% for the fungus Alternaria spp for the same treatment and the same concentration.

Current crop disease VQA models primarily focus on object counting and detection. However, accurately identifying various disease stages and determining control measures re quire additional knowledge beyond images, including information about control methods and pathogen details. To address this, the VQA dataset relies on the images and questions to retrieve relevant external knowledge. To realize the VQA task of crop diseases external knowledge, we construct the Visual Question Answer Model Based on Crop Diseases External Knowledge for Smart Agriculture (CDEK). CDEK integrates two categories of external knowledge on 66 common dicotyledonous crop diseases by utilizing large language models and agricultural knowledge repositories to enhance knowledge retrieval. This integration enhances the richness of external knowledge repositories. En hancing fine-grained image understanding in CDEK through the utilization of Stack Self-Attention (SSA), utilising Cross Attention and contrastive learning of two external knowledge, with a focus on emphasizing image-related semantic information during training. Finally, an automatic patrol disease detection robot is constructed based on Tensor Processing Unit (TPU) devices and the CDEK model. CDEK achieves an accuracy of 61.7% on the publicly available dataset OK-VQA, surpassing the previous state-of-the-art by 5.1%. Furthermore, we construct the OKiCD-VQA dataset for crop diseases external knowledge and achieve an accuracy of 89.36% using CDEK. A series of ablation experiments are conducted on various modules, the effectiveness of CDEK is demonstrated through extensive experimentation. Contributing solutions to the sustainable development of smart agriculture.

BACKGROUND:Bats are critical reservoirs of coronaviruses, including zoonotic strains, yet gaps remain in understanding coronavirus diversity within Ethiopia's understudied bat populations. This study investigated coronavirus prevalence and genetic diversity in bats from Afar and Dire Dawa, Eastern Ethiopia. METHODS:A cross-sectional study was conducted from November 2015 to June 2016 in purposively selected sites of Afar and Dire Dawa, Ethiopia, where bats were captured using 12 × 3-m mist nets. Ribonucleic acid (RNA) was screened by real-time polymerase chain reaction (RT-PCR) targeting the RNA-dependent RNA polymerase (RdRp) gene, with confirmatory PCR for the open reading frame 1a (ORF1a) region. RESULTS:A total of 141 bats were captured, including 129 Chaerephon pumilus, 11 Epomophorus labiatus and 1 Mops condylurus, from which 278 oropharyngeal swabs, rectal swabs, and urine specimens were collected. Coronaviruses were detected in eight bats (5.7%): five C. pumilus, two E. labiatus and one M. condylurus. Of these, three were detected from rectal swabs, three from oral swabs and two from urine samples. A higher proportion of adult bats (6.7%, 6/89) carried the virus compared to juveniles, with similar proportions of males (5.5%, 3/55) and females (5.8%, 5/86) testing positive. Sequencing revealed six Alphacoronaviruses (75%) and two Betacoronaviruses (25%; lineage D). CONCLUSION:The study contributes important baseline data on coronavirus diversity in the area. Continued genomic surveillance, including full-genome sequencing and broader ecological sampling, is essential to advance understanding of coronavirus evolution and maintenance within bat populations.

Date palms (Phoenix dactylifera) are a vital crop in the Middle East, particularly in Iraq, where infestations by the dubas bug (Ommatissus lybicus) and the white date scale (Parlatoria blanchardi) significantly threaten yield and fruit quality. This study assessed the efficacy of a single soil drench application of thiamethoxam (Actara 240 SC) for controlling these pests in two cultivars, Barhi and Brem, under field conditions. Treatments resulted in a substantial and sustained reduction in pest populations compared to untreated controls over a 120-day period. Residue analysis using gas chromatography demonstrated systemic absorption and gradual dissipation of thiamethoxam in leaves and fruits, with residues declining below detection limits by 120 days after treatment. The temporal correlation between residue decline and pest suppression underscores the prolonged protective effect of root drenching. These findings support the use of a single root drench as an effective, long-lasting, and potentially more sustainable pest management strategy that reduces the need for repeated foliar applications, thereby lowering labor and chemical inputs.

This study investigates the spatiotemporal variability and trends of rainfall and temperature in the Ejerie district, Ethiopia, focusing on the Dega and Weyina Dega agroecological zones. Gridded monthly rainfall data (1990-2020) and temperature records (1990-2018) from the Ethiopian Meteorology Institute were analyzed at a 4 & times; 4 km resolution. Variability was assessed using mean, standard deviation, coefficient of variation (CV), precipitation concentration index (PCI), and standardized rainfall anomaly (SRA). Trend analysis was conducted using the Mann-Kendall test (MK test) and innovative trend analysis (ITA). Results showed that Dega receives 1245 mm of annual rainfall, while Weyina Dega receives 907.8 mm, with both regions exhibiting low rainfall variability. Rainfall is concentrated in the summer season, followed by Belg and Bega, with notable monthly variability in November and December. The PCI analysis indicated irregular rainfall distributions, with Dega showing 87.09% irregular and 9.68% strongly irregular distributions, while Weyina Dega showed 83.87% irregular and 3.23% strongly irregular distributions. SRA analysis revealed that 83.27% of Dega and 84.48% of Weyina Dega did not experience drought, with minor drought occurrences observed in both regions. Temperature analysis showed significant seasonal differences, with Dega experiencing cooler temperatures than Weyina Dega, which has a warmer climate. Both the MK test and ITA methods yielded consistent temperature trends, with the exception of minor discrepancies in Bega season temperatures. These findings emphasize the importance of localized climate studies to inform area-specific adaptation strategies for agriculture and water resource management in the region and climate resilient livelihood.