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    Optimizing structural parameters for accurate prediction of height and diameter relationships in Himalayan pine using artificial intelligence based neural networks
    (Pakistan Journal of Botany, 2024-11-14) M. Iqbal Jeelani; Souad Baowidan; Fehim Jeelani Wani; Tauseef A. Bhat; Farheen Naqash; Elsiddig Idriss Mohamed; Fatma Mansour; Nahla Zidan; Mohamed Sakran; Alaa Baazeem; Mansha Gul
    Accurate prediction of height and diameter relationships in the context of Himalayan Pine (Pinus wallichiana) holds immense ecological significance. Leveraging the capabilities of Artificial Intelligence (AI) through neural network models provides a promising avenue for achieving such predictions. This study focuses on investigating the impact of structural parameters on the accuracy of AI-based neural network models designed specifically for this purpose. By identifying the optimal combination of parameters such as the number of layers, neurons per layer, and the choice of activation functions, the research aims to enhance the precision of predictions regarding the growth patterns of Himalayan Pine. The results of this study have practical implications for ecological research and conservation efforts in the Himalayan ecosystem. By optimizing the structural parameters of AI-based neural network models, researchers can achieve more accurate predictions of height and diameter relationships for Himalayan Pine. Such predictions are instrumental for informed decision-making regarding forest management, conservation strategies, and environmental sustainability.
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    Organic weed management can improve rice-maize rotation performances under conservation agriculture
    (Pakistan Journal of Botany, 2024-11-14) Subhra Sahoo; Dhirendra Kumar Roy; Shivani Ranjan; Sumit Sow; Smruti Ranjan Padhan; Alaa Baazeem; Omer Konuşkan; Zeki Erden; Çağdaş Can Toprak; Ayman El Sabagh
    A two-year field experiment was carried out to ascertain the influence of organic weed management (OWM) on the crop performance and productivity of rice–maize rotation under conservation agriculture. The experiment comprised of four tillage practices as main plots and five OWM treatments as subplots arranged in split-plot design with three replication. The tillage management treatments included ZTR fb ZTM: zero-tillage (ZT) direct seeded rice (DSR) followed by (fb) ZT-maize, PBDSR+R fb PBDSM+R: DSR fb maize both in permanent bed (PB) with residue incorporation, PBDSR-R fb PBDSM-R: DSR fb maize both in PB without residue and CTR fb CTM: conventionally tilled rice fb maize. In OWM, five treatments were as follows: UC: unweeded weed control, VM: vermicompost mulching, PVM: phosphorous (P) enriched VM, LM: live-mulch of Sesbania spp. in rice and Pisum sativum in maize, WF: weed-free check. The PBDSR+R fb PBDSM+R obtained a significantly higher plant height (18.9–19.7%), leaf area index (LAI) (24.0–24.6%), dry matter accumulation (DMA) (10.8– 11.3%) and crop growth rate (CGR) over CTR fb CTM in both rice and maize in all the growth stages. Moreover, PBDSR+R fb PBDSM+R recorded significantly higher grain yield (63.6 and 66.0 q ha-1) in rice and in maize (93.02 and 94.31 q ha-1) over other treatments in both years. Among the various OWM, LM reported significantly superior growth attributes viz. plant height, number of tillers m-2, leaf area index and dry matter accumulation in rice and maize and grain yield by 12.3–16% in rice and 7.4–8.5% in maize over VM across the years of study. The PBDSR+R fb PBDSM+R recorded and LM recorded significantly the highest net return and benefit-cost ratio throughout the study. The study highlights that residue incorporation under rice–maize rotation in PB led CA system along with LM enhanced productivity and profitability.