Bishal Nneupane publishes under the byline Bishal Neupane. This page connects both spellings to one research profile and summarizes the question, evidence, and main finding of each paper.
Nine peer-reviewed articles · 2026 · first author on three, corresponding author on all nine · three more accepted and in press
View complete record on ResearchGate Google Scholar ORCID 0009-0004-0394-3570
Article · Tropical Agroecosystems 7(2): 59–64 · October 2026
Randomised complete block trial of GA3 and NAA foliar sprays on okra cv. Parbati at the Agriculture Research Station, Tarahara, Sunsari. The 100 ppm GA3 + 100 ppm NAA combination produced the tallest plants and largest leaves, while NAA at 150 ppm alone gave the most pods per plant and the highest pod yield (9.55 t ha−1 against 3.39 t ha−1 in the control, a 181.7% increase).
DOI 10.26480/taec.02.2026.59.64 Plain-language summaryArticle · i TECH MAG 8: 77–83 · October 2026
Randomised complete block trial of nine spring rice genotypes against the released check Hardinath-1 at Gadhi-3, Sunsari. IR-19-A-5000 gave the highest grain yield (6.38 t ha−1) with the earliest maturity (117.3 days) and the lowest sterility (16.56%), and the yield ranking was driven by harvest index rather than total biomass.
DOI 10.26480/itechmag.08.2026.77.83 Plain-language summaryArticle · Tropical Agrobiodiversity 7(2): 128–134 · September 2026
Randomised complete block trial of black plastic, silver-on-black plastic, rice straw and sawdust mulches against bare soil on spring lettuce at Khumaltar, Lalitpur. Silver plastic kept aphids, cutworms and Helicoverpa armigera lowest, while rice straw favoured coccinellid predators but also mealybugs and cutworms. Rice straw and silver plastic gave the highest final-harvest yields (13.07 and 12.84 t ha−1 against 7.09 in the control), and sawdust returned the best benefit–cost ratio (1.83).
DOI 10.26480/trab.02.2026.128.134 Plain-language summaryArticle · Integrated Urban Food Systems: 67–75 · September 2026
Randomised complete block experiment on mature Bombay Green mango trees at the Tropical Horticulture Centre in Nawalpur, Sarlahi. GA3 at 35 ppm produced the highest fruit retention, BAP at 15 ppm produced the highest yield, and NAA at 35–50 ppm most improved sweetness and internal fruit quality.
DOI 10.26480/iufs.01.2026.67.75 Plain-language summaryArticle · Tropical Agrobiodiversity 7(2): 121–127 · September 2026
Household survey of 60 maize growers in Chitwan. Fall armyworm ranked among the top insect pests, yet insecticides were the only common response and no farmer reported a cultural or biological practice. Guidance came from fellow farmers and agrovets, not public extension.
DOI 10.26480/trab.02.2026.121.127 Plain-language summaryArticle · Tropical Agrobiodiversity 7(2): 113–120 · August 2026
Survey of 91 spring rice growers in Rautahat. Irrigation and seed availability were significantly associated with maturity preference, and sowing time affected production cost and productivity but not family income. Drought risk and high input cost were the leading constraints.
DOI 10.26480/trab.02.2026.113.120 Plain-language summaryArticle · Big Data In Agriculture 8(2): 89–95 · August 2026
Production economics of two F1 hybrid cucumbers from a survey of 60 farmers in Katahari and Jahada, Morang. Both were profitable (benefit–cost ratio 2.40 for Chandani, 2.19 for Raja), with intercultural labour the main driver of productivity. Lack of training and extension, and of storage, were the leading constraints.
DOI 10.26480/bda.02.2026.89.95 Plain-language summaryArticle · Tropical Agrobiodiversity 7(2): 83–90 · August 2026
Survey of 100 maize-farming households in Satyawati Rural Municipality, Gulmi, a PMAMP maize zone. Power tillers dominated tillage (69%) but post-harvest mechanisation reached only 13%. Farmers rated the bullock-drawn plough as the most agronomically effective, and the main barriers to adoption were lack of extension advice (66%) and training (44%) rather than cost (17%).
DOI 10.26480/trab.02.2026.83.90 Plain-language summaryReview · Tropical Agrobiodiversity 7(1): 61–68 · July 2026
Review of 2019–2025 evidence on rice mechanisation in Nepal. Land preparation (86–100%) and threshing (65–91%) are largely mechanised, but transplanting, weeding and fertiliser application remain manual. Training and subsidies were the strongest drivers of adoption, while land fragmentation, machinery cost, weak service networks and gendered access held it back. Spring rice is a promising target for mechanisation.
DOI 10.26480/trab.01.2026.61.68 Plain-language summaryAccepted / In press
Accepted June 2026 · Turkish Journal of Agriculture – Food Science and Technology (TURJAF) · In copyediting
Field experiment in a randomised complete block design testing NAA and GA3 concentrations on okra during the PMAMP Morang internship. GA3 at 100 ppm was the most effective treatment for growth and yield.
Accepted June 2026 · Turkish Journal of Agriculture – Food Science and Technology (TURJAF) · In copyediting
Field study comparing organic and inorganic fertiliser amendments on the growth and yield of spring rice in Nepal’s eastern Terai.
Accepted October 2026 · Turkish Journal of Agriculture – Food Science and Technology (TURJAF) · In copyediting
Field comparison of organic and inorganic fertiliser regimes on summer squash under mid-hill conditions in Nepal, assessing growth, yield and fruit quality responses.
NAXA Nepal · February 2025
Training in CROPGRO and CERES crop modules, genetic coefficient calibration for local varieties, simulation of climate–crop interactions and environmental stress responses, model validation against field experimental data, and integration of DSSAT outputs with the R statistical pipeline (tidyverse, agricolae) for ANOVA and variance analysis. Applied in crop physiology research and climate scenario analysis for Nepal’s subtropical terai conditions.
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Short notes on precision agriculture, remote sensing, AI technology, and data science, published on LinkedIn and clearly separated from the peer-reviewed record above.
I'm a student in Nepal with no MS, no research grant, and no institutional backing. Here's exactly how I'm positioning myself anyway — through Google Earth Engine, field experiments, applied ML, and public documentation. A specific problem is more credible than a general skillset.
Read on LinkedInA year ago, I was counting tillers in a rice field. Today, I have 8 manuscripts under review, analyze farmland from space using Sentinel-2 imagery, and build ML models to predict crop yield. The path forward is built one uncomfortable step at a time.
Read on LinkedInI learned GIS. Then remote sensing. Then machine learning. Each step felt like starting over. But now I see them as one system — and the moment I stopped learning them separately, everything accelerated.
Read on LinkedInGIS gave me spatial thinking. Remote sensing gave me field-scale visibility. ML gave me predictive power. For agriculture, the real power is in combining all three — not treating them as separate skills.
Read on LinkedIn