Organomineral fertilizer is used to improve and ameliorate the supply of nutrients in soils. Right and adequate application of fertilizers are determinants of its nutrient supply efficiency, which in turn enhances the vegetative growth and yield of cucumber. Field experiments were conducted at the Research Farm of the Federal University of Agriculture, Abeokuta, Nigeria, to assess the effects of variety and rate of organomineral fertilizer on cucumber growth and yield. Trials were conducted from June to August 2019 and repeated from September to November 2019. The cultivars were Poinsett, Greengo, and Monalisa. The rates of organomineral fertilizer were 0, 2.5, or 5.0 tons. ha⁻¹. The treatments were replicated three times. Cucumber vegetative characters, yield, and yield components were studied. ‘Greengo’ produced the most leaves, followed by ‘Monalisa’; ‘Poinsett’ produced the least. Application of 5.0 tons. ha⁻¹ organomineral fertilizer produced the longest vines and fruits. ‘Greengo’ had the earliest days to 50% flowering, followed by ‘Monalisa’; ‘Poinsett’ had the most days to 50% flowering. Plants treated with an application of 5.0 tons. ha⁻¹ organomineral fertilizer attained 50% flowering in 29 days, but in 30 days with an application of 2.5 tons. ha⁻¹ organomineral fertilizer; the control treatment attained 50% flowering in 33 days. Application of 5.0 tons. ha⁻¹ organomineral fertilizer produced the longest fruits, thicker fruit diameter, and highest fruit yield compared with 2.5 and 0 tons. ha⁻¹ of organomineral fertilizer treatments. The Greengo variety with application of 5.0 tons. ha⁻¹ of organomineral fertilizer is recommended for optimum growth and yield in south western Nigeria.

Highly nutritive and antioxidants-enriched okra (Abelmoschus esculentus) gets sub-optimal field yield due to the irregular germination coupled with non-synchronized harvests. Hence, the research aimed at assessing the combined impact of seed priming and field-level gibberellic acid (GA₃) foliar spray on the yield and post-harvest quality of okra. The lab studies were conducted using a complete randomized design (CRD), while the field trials were performed following a factorial randomized complete block design (RCBD) with three replications. Okra seeds were subjected to ten different priming methods to assess their impact on seed germination and seeding vigor. In the premier step, okra seeds were subjected to ten different priming methods, like hydro priming for 6, 12, and 18 h, halo priming with 3% NaCl at 35 ℃, 45 ℃, and 60 ℃, acid priming with 80% H₂SO₄ for 2.5, 5, and 10 min. Based on the observation, hydro priming for 12 h exhibited the best germination rate (90%), followed by halo seed priming at 60 ℃ and acid priming for 5 min. Furthermore, the halo priming at 60 ℃ demonstrated the greatest seedling vigor index (1965), whereas acid priming for 5 min resulted in favorable outcomes in terms of early emergence in 2.66 days. In addition, varying concentrations of GA₃ (0, 100, 200, and 300 ppm) were also administered to the best three primed seedlings for evaluating their field performance. The findings indicated that applying GA₃ at a concentration of 300 ppm to seedlings raised through acid priming (80% H₂SO₄ for 5 min) resulted in improved leaf length, reduced time to flowering (first and 50%) and harvest, increased pod diameter, individual pod weight, and yield per plant (735.16 g). Additionally, the treatment involving GA₃ at 300 ppm with halo priming (3% NaCl) at 60 ℃ exhibited the longest shelf life (21 days) of okra with the lowest levels of rotting (6.73%) and color change (1.12) in the polyethylene storage condition.

Ancient Minipe Anicut, Sri Lanka is world-famous for its engineering excellence. Due to its importance, conserving the ancient anicut, another anicut was constructed downstream in the 20th century. Nevertheless, the water diverted from the ancient anicut to the Minipe Left Bank (LB) Canal was kept as it was due to inherited agricultural importance. This research focuses on studying the contributions made by the adjacent catchment along the Minipe LB Canal. There are several level crossings along the Minipe Left Bank Canal from which the runoff of the local catchment flow into the Minipe LB Canal. Hydrologic Modeling System (HEC-HMS) is used to obtain the yield from each catchment into the Canal, which was compared with the annual diversions from Minipe anicut. The total yield from each stream has been compared with the annual diversion of the Minipe LB Canal from 2014 to 2020. The results obtained from this study reveal that there is sufficient water available for water augmentation in the basin, with an estimated annual average cumulative yield from the catchment of 453.6 MCM. This cumulative yield is 1.7 times the annual average diversion from the Mahaweli River, which is 271.9 MCM. With the findings, it is concluded that there is a potential to augment water from the catchment to address pertaining water shortages conveyance in the command area.

This study investigated the variability of climate parameters and food crop yields in Nigeria. Data were sourced from secondary sources and analyzed using correlation and multivariate regression. Findings revealed that pineapple was more sensitive to climate variability (76.17%), while maize and groundnut yields were more stable with low sensitivity (0.98 and 1.17%). Yields for crops like pineapple (0.31 kg/ha) were more sensitive to temperature, while maize, beans, groundnut, and vegetable yields were less sensitive to temperature with yields ranging from 0.15 kg/ha, 0.21 kg/ha, 0.18 kg/ha, and 0.12 kg/ha respectively. On the other hand, maize, beans, groundnut, and vegetable yields were more sensitive to rainfall ranging from 0.19kg/ha, 0.15kg/ha, 0.22 kg/ha, and 0.18 kg/ha respectively compared to pineapple yields which decreased with increase rainfall (−0.25 kg/ha). The results further showed that for every degree increase in temperature, maize, pineapple, and beans yields decreased by 0.48, 0.01, and 2.00 units at a 5 % level of significance, while vegetable yield decreased by 0.25 units and an effect was observed. Also, for every unit increase in rainfall, maize, pineapple, groundnut, and vegetable yields decreased by 3815.40, 404.40, 11,398.12, and 2342.32 units respectively at a 5% level, with an observed effect for maize yield. For robustness, these results were confirmed by the generalized additive and the Bayesian linear regression models. This study has been able to quantify the impact of temperature on food crop yields in the African context and employed a novel analytical approach combining the correlation matrix and multivariate linear regression to examine climate-crop yield relationships. The study contributes to the existing body of knowledge on climate-induced risks to food security in Nigeria and provides valuable insights for policymakers, farmers, government, and stakeholders to develop effective strategies to mitigate the impacts of climate change on food crop yields through the integration of climate-smart agricultural practices like agroforestry, conservation agriculture, and drought-tolerant varieties into national agricultural policies and programs and invest in climate information dissemination channels to help consider climate variability in agricultural planning and decision-making, thereby enhancing food security in the country.

Accurate prediction of US Treasury bond yields is crucial for investment strategies and economic policymaking. This paper explores the application of advanced machine learning techniques, specifically Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM) models, in forecasting these yields. By integrating key economic indicators and policy changes, our approach seeks to enhance the precision of yield predictions. Our study demonstrates the superiority of LSTM models over traditional RNNs in capturing the temporal dependencies and complexities inherent in financial data. The inclusion of macroeconomic and policy variables significantly improves the models’ predictive accuracy. This research underscores a pioneering movement for the legacy banking industry to adopt artificial intelligence (AI) in financial market prediction. In addition to considering the conventional economic indicator that drives the fluctuation of the bond market, this paper also optimizes the LSTM to handle situations when rate hike expectations have already been priced-in by market sentiment.

Uncontrolled economic development often leads to land degradation, a decline in ecosystem services, and negative impacts on community welfare. This study employs water yield (WY) modeling as a method for environmental management, aiming to provide a comprehensive understanding of the relationship between Land Use Land Cover (LULC), Land Use Intensity (LUI), and WY to support sustainable natural resource management in the Cisadane Watershed, Indonesia. The objectives include: (1) analyzing changes in WY for 2010, 2015, and 2021; (2) predicting WY for 2030 and 2050 under two scenarios—Business as Usual (BAU) and Protected Forest Area (PFA); (3) assessing the impacts of LULC and climate change on WY; and (4) exploring the relationship between LUI and WY. The Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) model calculates actual and predicted WY conditions, while the Coupling Coordination Degree (CCD) analyzes the LULC-WY relationship. Results indicate that the annual WY in 2021 was 215.8 × 108 m³, reflecting a 30.42% increase from 2010. Predictions show an increasing trend in WY under both scenarios for 2030 and 2050 with different magnitudes. Rainfall contributes 88.99% more dominantly to WY than LULC. Additionally, around 50% of districts exhibited unbalanced coordination between LUI and WY in 2010 and 2020. This study reveals the importance of ESs in sustainable watershed management amidst increasing demand for natural resources due to population growth.