Yazar "Hasnain, Muhammad Usama" seçeneğine göre listele

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    Effect of slow release nitrogenous fertilizers and biochar on growth, physiology, yield, and nitrogen use efficiency of sunflower under arid climate
    (Springer Heidelberg, 2022) Waqar, Muhammad; Habib-ur-Rahman, Muhammad; Hasnain, Muhammad Usama; Iqbal, Shahid; Ghaffar, Abdul; Iqbal, Rashid; Hussain, Muhammad Iftikhar
    Sunflower plants need nitrogen consistently and in higher amount for optimum growth and development. However, nitrogen use efficiency (NUE) of sunflower crop is low due to various nitrogen (N) losses. Therefore, it is necessary to evaluate the advanced strategies to minimize N losses and also improve sunflower productivity under arid climatic conditions. A field trial was conducted with four slow release nitrogenous fertilizers [SRNF (bacterial, neem, and sulfur-coated urea and N loaded biochar)] and three N levels (100% = 148 kg N-ha(-1), 80% = 118 kg N-ha(-1), and 60% = 89 kg N-ha(-1)) of recommended application (100%) for sunflower crop under arid climatic conditions. Results showed that neem-coated urea at 148 kg N-ha(-1) significantly enhanced crop growth rate (CGR) (19.16 g-m(-2)-d(-1)) at 60-75 days after sowing (DAS); leaf area index (2.12, 3.62, 5.97, and 3.00) at 45, 60, 75, and 90 DAS; and total dry matter (14.27, 26.29, 122.67, 410, and 604.33 g m(-2)) at 30, 45, 60, 75, and 90 DAS. Furthermore, higher values of net leaf photosynthetic rate (25.2 mu mol m(-2) -s(-1)), transpiration rate (3.66 mmol-s(-1)), and leaf stomatal conductance (0.39 mol-m(-2)-s(-1)) were recorded for the same treatment. Similarly, neem-coated urea produced maximum achene yield (2322 kg ha(-1)), biological yield (9000 kg-ha(-1)), and harvest index (25.8%) of the sunflower crop. Among various N fertilizers, neem-coated urea showed maximum NUE (20.20 kg achene yield kg(-1) N applied) in comparison to other slow release N fertilizers. Similarly, nitrogen increment N-60 showed maximum NUE (22.40 kg grain yield-kg(-1) N applied) in comparison to N-80 and N-100. In conclusion, neem-coated urea with 100% and 80% of recommended N would be recommended for farmers to get better sunflower productivity with sustainable production and to reduce the environmental nitrogen losses.
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    Effect of slow-release nitrogenous fertilizers on dry matter accumulation, grain nutritional quality, water productivity and wheat yield under an arid environment
    (Nature Portfolio, 2022) Ghafoor, Iqra; Rahman, Muhammad Habib Ur; Hasnain, Muhammad Usama; Ikram, Rao Muhammad; Khan, Mahmood Alam; Iqbal, Rashid; Hussain, Muhammad Iftikhar
    Slow release nitrogenous fertilizers can improve crops production and reduce the environmental challenges in agro-ecosystem. There is a need to test the efficiency and performance under arid climatic conditions. The study investigates the effect of slow-release fertilizers (urea, neem coated urea (NCU), sulfur coated urea (SCU) and bioactive sulfur coated urea (BSCU)) on the growth, productivity and grain nutritional qualities of wheat crop. Slow-release fertilizers (SRF) with nitrogen levels (130,117,104 and 94 kg ha(-1)) were applied with equal splits at sowing, 20 and 60 days after sowing (DAS). Research showed that the BSCU with 130 kg ha(-1) increased dry matter accumulation (1989 kg ha(-1)) after anthesis and grain yield 4463 kg ha(-1). The higher plant height (102 cm) was attained by 130 kg N ha(-1) SCU while the minimum (77.67 cm) recorded for 94 kg N ha(-1) as urea source. Maximum grain NPK concentrations (3.54, 0.66 and 1.07%) were recorded by BSCU 130 kg N ha(-1) application. While, the minimum NPK (0.77, 0.19 and 0.35%) were observed by Urea 94 kg N ha(-1). The high irrigation water use efficiency (WUE) recorded (20.92 kg ha(-1) mm(-1)) and a crop index of 25.52% by BSCU 130 kg N ha(-1) application. Research findings show that generally all SRF but particularly BSCU proved effective and can be recommended for wheat crop under arid environment.
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    Impact of climate change on agricultural production; Issues, challenges, and opportunities in Asia
    (Frontiers Media Sa, 2022) Habib-ur-Rahman, Muhammad; Ahmad, Ashfaq; Raza, Ahsan; Hasnain, Muhammad Usama; Alharby, Hesham F.; Alzahrani, Yahya M.; Bamagoos, Atif A.
    Agricultural production is under threat due to climate change in food insecure regions, especially in Asian countries. Various climate-driven extremes, i.e., drought, heat waves, erratic and intense rainfall patterns, storms, floods, and emerging insect pests have adversely affected the livelihood of the farmers. Future climatic predictions showed a significant increase in temperature, and erratic rainfall with higher intensity while variability exists in climatic patterns for climate extremes prediction. For mid-century (2040-2069), it is projected that there will be a rise of 2.8 degrees C in maximum temperature and a 2.2 degrees C in minimum temperature in Pakistan. To respond to the adverse effects of climate change scenarios, there is a need to optimize the climate-smart and resilient agricultural practices and technology for sustainable productivity. Therefore, a case study was carried out to quantify climate change effects on rice and wheat crops and to develop adaptation strategies for the rice-wheat cropping system during the mid-century (2040-2069) as these two crops have significant contributions to food production. For the quantification of adverse impacts of climate change in farmer fields, a multidisciplinary approach consisted of five climate models (GCMs), two crop models (DSSAT and APSIM) and an economic model [Trade-off Analysis, Minimum Data Model Approach (TOAMD)] was used in this case study. DSSAT predicted that there would be a yield reduction of 15.2% in rice and 14.1% in wheat and APSIM showed that there would be a yield reduction of 17.2% in rice and 12% in wheat. Adaptation technology, by modification in crop management like sowing time and density, nitrogen, and irrigation application have the potential to enhance the overall productivity and profitability of the rice-wheat cropping system under climate change scenarios. Moreover, this paper reviews current literature regarding adverse climate change impacts on agricultural productivity, associated main issues, challenges, and opportunities for sustainable productivity of agriculture to ensure food security in Asia. Flowing opportunities such as altering sowing time and planting density of crops, crop rotation with legumes, agroforestry, mixed livestock systems, climate resilient plants, livestock and fish breeds, farming of monogastric livestock, early warning systems and decision support systems, carbon sequestration, climate, water, energy, and soil smart technologies, and promotion of biodiversity have the potential to reduce the negative effects of climate change.
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    The use of Multispectral Radio-Meter (MSR5) data for wheat crop genotypes identification using machine learning models
    (Nature Portfolio, 2023) Jamil, Mutiullah; Rehman, Hafeezur; Zaheer, Muhammad Saqlain; Tariq, Aqil; Iqbal, Rashid; Hasnain, Muhammad Usama; Majeed, Asma
    Satellite remote sensing is widely being used by the researchers and geospatial scientists due to its free data access for land observation and agricultural activities monitoring. The world is suffering from food shortages due to the dramatic increase in population and climate change. Various crop genotypes can survive in harsh climatic conditions and give more production with less disease infection. Remote sensing can play an essential role in crop genotype identification using computer vision. In many studies, different objects, crops, and land cover classification is done successfully, while crop genotypes classification is still a gray area. Despite the importance of genotype identification for production planning, a significant method has yet to be developed to detect the genotypes varieties of crop yield using multispectral radiometer data. In this study, three genotypes of wheat crop (Aas-'2011', 'Miraj-'08', and 'Punjnad-1) fields are prepared for the investigation of multispectral radio meter band properties. Temporal data (every 15 days from the height of 10 feet covering 5 feet in the circle in one scan) is collected using an efficient multispectral Radio Meter (MSR5 five bands). Two hundred yield samples of each wheat genotype are acquired and manually labeled accordingly for the training of supervised machine learning models. To find the strength of features (five bands), Principle Component Analysis (PCA), Linear Discriminant Analysis (LDA), and Nonlinear Discernment Analysis (NDA) are performed besides the machine learning models of the Extra Tree Classifier (ETC), Random Forest (RF), Support Vector Machine (SVM), Decision Tree (DT), Logistic Regression (LR), k Nearest Neighbor (KNN) and Artificial Neural Network (ANN) with detailed of configuration settings. ANN and random forest algorithm have achieved approximately maximum accuracy of 97% and 96% on the test dataset. It is recommended that digital policymakers from the agriculture department can use ANN and RF to identify the different genotypes at farmer's fields and research centers. These findings can be used for precision identification and management of the crop specific genotypes for optimized resource use efficiency.