AMA, Agricultural Mechanization in Asia, Africa and Latin America
AMA, Agricultural Mechanization in Asia, Africa and Latin America (AMA) (issn: 00845841) is a peer reviewed journal first published online after indexing scopus in 1982. AMA is published by Farm Machinery Industrial Research Corp and Shin-Norinsha Co. AMA publishes every subjects of general engineering and agricultural engineering.
WOS Indexed (2025)
27 Nov 2025 (Vol - 56 , Issue- 11 )
30 Nov 2025 (Vol - 56 , Issue 11 )
Aim and Scope :
AMA, Agricultural Mechanization in Asia, Africa and Latin America
AMA, Agricultural Mechanization in Asia, Africa and Latin America (ISSN: 00845841) is a peer-reviewed journal. The journal covers Agricultural and Biological Sciences and all sort of engineering topic. the journal's scopes are in the following fields but not limited to:
Agricultural and Biological Sciences
Electrical Engineering and Telecommunication
Electronic Engineering
Computer Science & Engineering
Civil and architectural engineering
Mechanical and Materials Engineering
Transportation Engineering
Industrial Engineering
Industrial and Commercial Design
Information Engineering
Chemical Engineering
Food Engineering
STANDARDIZATION OF DRIS NORMS FOR LITCHI ORCHARDS UNDER JAMMU REGION
To compute the diagnosis and recommendation integrated system (DRIS) criteria, information on the leaf mineral composition, available soil nutrients, and associated mean fruit yield of 50 litchi orchards in the Jammu and Kathua regions of the Jammu area in 2022 and 2023 was used. Due to the DRIS norms derived from leaf analysis, the ideal ranges for macronutrient concentrations were as follows: Phosphorus (P) varies between 0.13 - 0.23%, potassium (K) ranges from 0.73 - 1.04%, sulphur (S) ranges from 0.18 - 0.27%, calcium (Ca) varies at 1.72 - 1.93%, while magnesium ranges from 0.35 - 0.55%. These ranges are appropriate for micronutrients: Molybdenum (Mo): 1.8–2.81%; boron (B): 6.25–1.96%; zinc (Zn): 9.34–11.93 ppm; iron (Fe): 89.24–156.98 ppm; copper (Cu): 7.24–10.42 ppm; manganese (Mn): 8.97–21.64 ppm. Fruit yields within these nutritional ranges ranged from 16.55 to 114.24 kg per tree. Similarly, DRIS indices for soil fertility were created using soil samples taken at 0–15, 15–30, and 30–45 cm, corresponding to comparable fruit yield levels. The following were found to be the optimal limits of nutrients available in the soil (mg/kg or kg/ha): 8.84–15.46 kg/ha in phosphorus (P), 93.17–199.15 kg/ha in potassium (K), 6.79–11.42 mg/kg during sulphur (S), 0.35–4.36 mg/kg with zinc (Zn), 2.81–13.42 mg/kg in iron (Fe), 1.44–4.43 mg/kg in copper (Cu), and 0.28–2.84 mg/kg in manganese (Mn) boron (B) at 28.90-51.80 mg/kg, and molybdenum (Mo) at 0.18-0.30 mg/kg. According to leaf-based DRIS indices, 55% of the orchards had potassium deficiencies, followed by nitrogen (25%), calcium (15%), & magnesium (5%). On the other hand, magnesium was found to be excessive in 45% of the orchards, followed by nitrogen, phosphorus, potassium, and calcium.
Incubating Rural Innovation: Agri-Startups and the Shifting Paradigms of Indian Agriculture
Agricultural entrepreneurs are turning to a range of technological tools—from artificial intelligence (AI), the Internet of Things (IoT), remote sensing and blockchain, big data analytics, mobile apps, and GIS mapping—to fill significant gaps in the agricultural value chain. These enterprises are transforming agricultural services using digital payment systems, precision farming, real-time weather forecasting, and farm-to-fork logistics by adopting a user-centric, scalable, and affordable model. Sustainable Development Goals are viewed in this paper as the agrarian start-ups the sustainability of which includes the attainment of zero hunger (SDG 2), decent work and economic growth (SDG 8), industrial and innovation (SDG 9), and climate action (SDG 13). The ecosystem, however, faces several difficulties. Problems include insufficient last-mile digital infrastructure, low digital literacy among farmers, restricted access to early-stage investment, regulatory restrictions, and scalability limits continue to hinder the widespread adoption and efficacy of agritech solutions. This article carefully examines these issues and offers institutional, regulatory, and investment-level remedies to help agri-entrepreneurship flourish. This paper claims that agri-startups are critical for India's agricultural modernization and rural revival. By promoting innovation at the local level and providing data-driven, scalable solutions, these businesses are generating commercial value while also enhancing environmental resilience, income, and food security. As India works to be a world leader in sustainable agriculture, understanding and supporting the agri-startup ecosystem becomes increasingly crucial.
EFFECT OF PHOSPHORUS AND SULPHUR MANAGEMENT ON YIELD, NUTRIENT UPTAKE, AND NUTRIENT USE INDICES IN RICE BEAN (VIGNA UMBELLATA L.)
The Rice Bean (RB) (Vigna umbellata L.), which is native to South and Southeast Asia, is an annual underutilized grain legume crop that belongs to the family Fabaceae. It has a higher nutritional quality compared to many other legumes within the Vigna family. Yet, there is a lack of understanding of the impact of plant nutrients on the diverse attributes of RBs. Therefore, the research aims to examine the effect of diverse levels of Phosphorus (P) along with Sulphur (S) on the yield, Nutrient Uptake (NU), and nutrient use indices of RB crops. During the Kharif seasons of 2017-18 and 2018-19, a field experiment is conducted at an agricultural research farm in West Bengal. A total of 3 diverse levels of P and S are tested with Factorial Randomized Block Design (FRBD) (3×3+1 factorial). As per the outcome, an application of 30kg/ha S along with 80kg/ha P has recorded higher yield, nutrient content and uptake, and nutrient use indices of RB crop.
DEEP LEARNING-DRIVEN STREAM FLOW FORECASTING: A NOVEL APPROACH
Precise streamflow forecasting is essential for managing water resources, controlling flooding, planning irrigation, and guaranteeing agricultural sustainability. This study employs 38 years of monthly flow of the stream data from the Cholachagudda dam on the Malaprabha River, India, to evaluate and compare the performance of deep learning models, including CNN, RNN, LSTM, GRU, and a Transformer-based architecture. With the lowest RMSE (76.02), MAE, and quantile loss (20.72), the Transformer model performs better than alternative topologies, according to the dataIt overcomes the drawbacks of sequential models like LSTM and GRU by successfully identifying complicated temporal patterns and long-range relationships through its self-attention mechanism. This study highlights the challenges of predicting streamflow in non-linear hydrological systems, particularly for models limited to temporal data. By offering a thorough assessment, this study highlights the revolutionary potential of deep learning in hydrological forecasting and establishes the foundation for future developments in streamflow prediction techniques.
Characterization of Thanjavur Black Goats of Tamil Nadu
The habitat, distribution, morphology, morphometric and production performance of the Thanjavur Black goats were studied by a pretested interview schedule in the eastern districts of Tamil Nadu. A study was undertaken on 250 randomly selected goat flocks in Thanjavur, Nagapattinam, Thiruvarur, Tiruchirappalli and Pudukkottai districts of Tamil Nadu. Thanjavur Black goat is a compact, medium-sized, black-coloured animal. Coat is uniformly black and covered with short, straight black hair. The least squares means of height at withers, chest girth and body length of an adult goat were 61.59 ± 0.17, 59.71 ± 0.21 and 58.08 ± 0.25 cm respectively. The least squares means for body weight at birth, 3, 6, 9, 12 months and adult goats were 1.82 ± 0.02, 5.99 ± 0.05, 9.30 ± 0.07, 12.12 ± 0.09, 14.16 ± 0.08; and 22.63 ± 0.22 kg respectively. In all age groups of Thanjavur Black goats, the statistical analysis revealed a highly significant (P <0.01) effect of sex on body weight were observed. The overall dressing percentage was 51.02% at 20.63 months. The average age at first mating, age at first kidding and kidding intervals were 9.58 ± 0.10, 14.93 ± 0.10 and 7.89 ± 0.15 months respectively. The life time number of kidding in a doe was 6.66 ± 0.09. The average litter size of the does pooled over parities was 1.71 ± 0.03 and the incidence of multiple births was 57.21 per cent. This indigenous germplasm needs to be recognized by the NBAGR, Karnal as a separate breed.