Organic and Inorganic Mulches Combination Improves the Productivity, Quality and Profitability of Rainfed Potato in the Temperate Himalayan Region

Zahed, Zahedullah; Mufti, Shahnaz; Kumar, Shamal Shasang; Wani, Owais Ali; Mushtaq, Faheema; Rasool, Rovidha Saba; Abidi, Ishfaq; Gaber, Ahmed; Hossain, Akbar

doi:10.1007/s10343-022-00650-1

Cited by 11 publications

(8 citation statements)

References 46 publications

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“…Similar findings have been reported by [66,67], who found a considerable increase in AC with the increase in pyrolysis temperature. The increase in the AC may be attributed to the progressive concentration of inorganic constituents [68], which also was revealed by [69].…”

Section: Effect Of Feedstock and Pyrolysis Temperature On Proximate A...mentioning

confidence: 71%

Mitigating Soil Erosion through Biomass-Derived Biochar: Exploring the Influence of Feedstock Types and Pyrolysis Temperature

Wani,

Akhter,

Kumar

et al. 2023

Land

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Soil erosion is one of the major emerging threats to the Himalayan ecosystem. There is a dearth of diverse, cost-effective erosion control measures in the region. In the Himalayan region, where agriculture plays a pivotal role in local livelihoods and environmental stability, the management of soil erosion is of paramount importance. Hence, this study investigates the impact of biochar application on soil erosion and its related indices in the temperate Himalayas of India. This study employs a combination of physicochemical analysis and field experiments to assess the influence of biochar on soil erodibility. The research objectives include an examination of the influence of different temperature pyrolyzed biomasses and varying application rates on soil erodibility indices, viz., dispersion ratio (DR), percolation ratio (PR), clay ratio (CR), erosion ratio (ER), and mean weight diameter (MWD), considering two distinct fertilizer regimes. This study yielded quantitative results that shed light on the impact of various soil amendments and application rates on soil erodibility in the temperate Himalayas. Results showed that the mean values of the DR exhibited by amendment levels NB, AB400, AB600, RAC, DW400, DW600, and RDW were 0.37, 0.35, 0.51, 0.44, 0.51, 0.47, and 0.91, respectively. The mean values of DR for different amendment levels varied, with RDW exhibiting the highest erodibility at 0.91, while DW400 and DW600 demonstrated less soil disturbance, making them promising choices for soil erosion mitigation. Notably, the application of pyrolyzed weed residue improved soil erodibility, whereas AB600 resulted in increased soil erosion due to aggregate disintegration, as indicated by the MWD. Aquatic weed residues and apple wood chips applied without pyrolysis increased the soil erodibility, while pyrolyzed residues improved soil erodibility. The DR was 0.41 at the high application rate, 0.48 at the medium rate, and 0.61 at the low application rate. Among application rates, low rates (1 t ha−1) had the highest DR, followed by medium rates (2 t ha−1) and high rates (3 t ha−1). The no-fertilizer level exhibited higher DR (0.49) compared to the fertilized level (0.38). Results inferred that the application of AB400 °C at 3 t ha−1 can be adopted to minimize soil erosion and maintain ecological security in the temperate Himalayas.

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Section: Effect Of Feedstock and Pyrolysis Temperature On Proximate A...mentioning

confidence: 71%

Mitigating Soil Erosion through Biomass-Derived Biochar: Exploring the Influence of Feedstock Types and Pyrolysis Temperature

Wani,

Akhter,

Kumar

et al. 2023

Land

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“…Application of RDN led to lower LAI and DMA in comparison with a NE®+SPAD meter-based N application due to various losses in the rice field and lesser availability to crops. The NE®+SPAD meter-based N application resulted in higher LAI and accumulated 8.3% greater dry matter over RDN due to synchronization of crop needs and the supply of N and, thus, better crop growth due to greater nutrient availability [46,47]. Need-based N supplementation by regular monitoring of the leaf greenness index using the SPAD meter maintained an optimum level of N inside the plant tissue.…”

Section: Growth Yield Attributes and Yieldsmentioning

confidence: 99%

Co-Implementation of Tillage, Precision Nitrogen, and Water Management Enhances Water Productivity, Economic Returns, and Energy-Use Efficiency of Direct-Seeded Rice

et al. 2022

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The sustainability of conventional rice (Oryza sativa L.) production systems is often questioned due to the over-mining of groundwater and environmental degradation. This has led to the development of cost-effective, resource-efficient, and environmentally clean rice production systems by optimizing water and nitrogen (N) use. Hence, a 2-year field study (2019 and 2020) was conducted at the ICAR–Indian Agricultural Research Institute, New Delhi, to assess the effect of precision N and water management strategies on growth, land, and water productivity, as well as energy-use efficiency in scented direct-seeded rice (DSR). Two crop establishment methods, conventional-till DSR (CT-DSR) and zero-till DSR (ZT-DSR) along with three irrigation scenarios (assured irrigation (irrigation after 72 h of the drying of surface water), irrigation at 20% depletion of available soil moisture (DASM), and 40% DASM+Si (80 kg ha−1)) were assigned to the main plots; three N management options, a 100% recommended dose of N (RDN): 150 kg ha−1; Nutrient Expert® (NE®)+leaf color chart (LCC) and NE®+soil plant analysis development (SPAD) meter-based N management were allocated to sub-plots in a three-time replicated split-plot design. The CT-DSR produced 1.4, 11.8, and 89.4, and 2.4, 18.8, and 152.8% more grain yields, net returns, and net energy in 2019 and 2020, respectively, over ZT-DSR. However, ZT-DSR recorded 8.3 and 10.7% higher water productivity (WP) than CT-DSR. Assured irrigation resulted in 10.6, 16.1 16.9, and 8.1 and 12.3, 21.8 20.6, and 6.7% higher grain yields, net returns, net energy, and WP in 2019 and 2020, respectively, over irrigation at 20% DASM. Further, NE®+SPAD meter-based N management saved 27.1% N and recorded 9.6, 18.3, 16.8, and 8.3, and 8.8, 21.7, 19.9, and 10.7% greater grain yields, net returns, net energy, and WP over RDN in 2019 and 2020, respectively. Thus, the study suggested that the NE®+SPAD-based N application is beneficial over RDN for productivity, resource-use efficiency, and N-saving (~32 kg ha−1) both in CA-based and conventionally cultivated DSR. This study also suggests irrigating DSR after 72 h of the drying of surface water; however, under obviously limited water supplies, irrigation can be delayed until 20% DASM, thus saving two irrigations, which can be diverted to additional DSR areas.

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“…Mobile applications, leveraging AI, empower citizens to contribute to environmental monitoring, promoting a decentralized approach to data collection (Sahil et al, 2023). These recent strides underscore the transformative potential of AI and ML in reshaping pollution mitigation strategies, steering towards a more sustainable and resilient future (Zahed et al, 2022).…”

Section: Other Applicationsmentioning

confidence: 99%

Artificial intelligence and IoT driven technologies for environmental pollution monitoring and management

Popescu,

Mansoor,

Wani

et al. 2024

Front. Environ. Sci.

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Detecting hazardous substances in the environment is crucial for protecting human wellbeing and ecosystems. As technology continues to advance, artificial intelligence (AI) has emerged as a promising tool for creating sensors that can effectively detect and analyze these hazardous substances. The increasing advancements in information technology have led to a growing interest in utilizing this technology for environmental pollution detection. AI-driven sensor systems, AI and Internet of Things (IoT) can be efficiently used for environmental monitoring, such as those for detecting air pollutants, water contaminants, and soil toxins. With the increasing concerns about the detrimental impact of legacy and emerging hazardous substances on ecosystems and human health, it is necessary to develop advanced monitoring systems that can efficiently detect, analyze, and respond to potential risks. Therefore, this review aims to explore recent advancements in using AI, sensors and IOTs for environmental pollution monitoring, taking into account the complexities of predicting and tracking pollution changes due to the dynamic nature of the environment. Integrating machine learning (ML) methods has the potential to revolutionize environmental science, but it also poses challenges. Important considerations include balancing model performance and interpretability, understanding ML model requirements, selecting appropriate models, and addressing concerns related to data sharing. Through examining these issues, this study seeks to highlight the latest trends in leveraging AI and IOT for environmental pollution monitoring.

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Organic and Inorganic Mulches Combination Improves the Productivity, Quality and Profitability of Rainfed Potato in the Temperate Himalayan Region

Cited by 11 publications

References 46 publications

Mitigating Soil Erosion through Biomass-Derived Biochar: Exploring the Influence of Feedstock Types and Pyrolysis Temperature

Mitigating Soil Erosion through Biomass-Derived Biochar: Exploring the Influence of Feedstock Types and Pyrolysis Temperature

Co-Implementation of Tillage, Precision Nitrogen, and Water Management Enhances Water Productivity, Economic Returns, and Energy-Use Efficiency of Direct-Seeded Rice

Artificial intelligence and IoT driven technologies for environmental pollution monitoring and management

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