Búsqueda
Autor: Timothy Joseph Krupnik
Timothy Joseph Krupnik (2023)
Fall Armyworm (FAW), Spodoptera frugiperda (Lepidoptera: Noctuidae), native to the Americas, is a polyphagous insect pest feeding on more than 350 plant species. We studied the developmental and demographic parameters of the maize (Zea mays) strain of FAW on rice (Oryza sativa), and compared the results with its prime host, maize. The developmental period from egg to adult among rice varieties did not differ significantly; however, it did differ significantly between rice and maize, as feeding on rice rather than maize extends development duration of FAW larvae by 15.15%. FAW larvae collected and reared on maize were found to be of significantly higher weight than those reared on rice at two sequential dates of their development; pupal weight however was observed as statistically similar between these two host crops. Regardless of the host, female adults always emerged before males; in maize, female FAW appeared 3.36 days earlier than males. Females derived from rice had longer pre-oviposition periods and shorter oviposition ones than those derived from maize. In rice and maize, the age-specific fecundity rate (mx) peaked at 40 days and 33 days, respectively. When the Fall Armyworm consumed maize instead of rice, there was an increase in the reproduction rate (R 0), the intrinsic rate of natural increase (rm), and the finite rate of increase (λ). For instance, when FAW fed on rice, the rm value was 0.121, whereas it rose to 0.173 when FAW fed on maize. Feeding on rice instead of maize resulted in significantly longer mean length of generation (tG) and doubling time (tD) for the fall armyworm (FAW). This suggests that it took a longer time for the FAW population to double when it was fed rice under controlled greenhouse conditions. In summary, our research suggests that FAW can survive and complete its life cycle on rice plants and on multiple varieties of rice in Bangladesh. However, field verification is necessary before drawing strong conclusions as to the risk posed by FAW in rice. This requires additional studies of FAW and associated insect community dynamics under non-controlled conditions and in the context of multi-species interactions in Asian rice fields.
Artículo
Invasive Pest Life Table Parameters CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA HOST PLANTS PESTS RICE SPODOPTERA FRUGIPERDA FALL ARMYWORMS
Mustafa Kamal Timothy Joseph Krupnik (2024)
High-resolution mapping of rice fields is crucial for understanding and managing rice cultivation in countries like Bangladesh, particularly in the face of climate change. Rice is a vital crop, cultivated in small scale farms that contributes significantly to the economy and food security in Bangladesh. Accurate mapping can facilitate improved rice production, the development of sustainable agricultural management policies, and formulation of strategies for adapting to climatic risks. To address the need for timely and accurate rice mapping, we developed a framework specifically designed for the diverse environmental conditions in Bangladesh. We utilized Sentinel-1 and Sentinel-2 time-series data to identify transplantation and peak seasons and employed the multi-Otsu automatic thresholding approach to map rice during the peak season (April–May). We also compared the performance of a random forest (RF) classifier with the multi-Otsu approach using two different data combinations: D1, which utilizes data from the transplantation and peak seasons (D1 RF) and D2, which utilizes data from the transplantation to the harvest seasons (D2 RF). Our results demonstrated that the multi-Otsu approach achieved an overall classification accuracy (OCA) ranging from 61.18% to 94.43% across all crop zones. The D2 RF showed the highest mean OCA (92.15%) among the fourteen crop zones, followed by D1 RF (89.47%) and multi-Otsu (85.27%). Although the multi-Otsu approach had relatively lower OCA, it proved effective in accurately mapping rice areas prior to harvest, eliminating the need for training samples that can be challenging to obtain during the growing season. In-season rice area maps generated through this framework are crucial for timely decision-making regarding adaptive management in response to climatic stresses and forecasting area-wide productivity. The scalability of our framework across space and time makes it particularly suitable for addressing field data scarcity challenges in countries like Bangladesh and offers the potential for future operationalization.
Artículo
Synthetic Aperture Radar Random Forest Boro Rice In-Season Maps CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA SAR (RADAR) RICE FLOODING CLIMATE CHANGE
Timothy Joseph Krupnik Jeroen Groot (2024)
We investigated alternative cropping and feeding options for large (>10 cows), medium (5–10 cows) and small (≤4 cows) mixed crop – livestock farm types, to enhance economic and environmental performance in Jhenaidha and Meherpur districts – locations with increasing dairy production – in south western Bangladesh. Following focus group discussions with farmers on constraints and opportunities, we collected baseline data from one representative farm from each farm size class per district (six in total) to parameterize the whole-farm model FarmDESIGN. The six modelled farms were subjected to Pareto-based multi-objective (differential evolution algorithm) optimization to generate alternative dairy farm and fodder configurations. The objectives were to maximize farm profit, soil organic matter balance, and feed self-reliance, in addition to minimizing feed costs and soil nitrogen losses as indicators of sustainability. The cropped areas of the six baseline farms ranged from 0.6 to 4.0 ha and milk production per cow was between 1,640 and 3,560 kg year−1. Feed self-reliance was low (17%–57%) and soil N losses were high (74–342 kg ha−1 year−1). Subsequent trade-off analysis showed that increasing profit and soil organic matter balance was associated with higher risks of N losses. However, we found opportunities to improve economic and environmental performance simultaneously. Feed self-reliance could be increased by intensifying cropping and substituting fallow periods with appropriate fodder crops. For the farm type with the largest opportunity space and room to manoeuvre, we identified four strategies. Three strategies could be economically and environmentally benign, showing different opportunities for farm development with locally available resources.
Artículo
Ruminant Feed Pareto-Based Optimization Farm Bioeconomic Model CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA RUMINANT FEEDING BIOECONOMIC MODELS MIXED CROPPING FARMS LIVESTOCK
User manual: How to use Agvisely to generate climate service advisories for livestock in Bangladesh
T.S Amjath-Babu Timothy Joseph Krupnik (2023)
The Agvisely digital service for livestock integrates location-specific meteorological forecasts generated by the Bangladesh Meteorological Department (BMD) with species specific biological thresholds for weather variables (Temperature, rainfall, and temperature-humidity index (THI). When a biological threshold is to be breached in next five days' forecast, the system automatically generates location-specific management advice for livestock farmers. Advisories are based on a decision tree developed by the Bangladesh Livestock Research Institute (BLRI) and CIMMYT. Agvisely is a smart phone app and web-based service developed by the International Maize and Wheat Improvement Center (CIMMYT) CIMMYT with the support of USAID, securing the Food Systems of Asian Mega- Deltas (AMD) for Climate and Livelihood Resilience and the Transforming Agrifood Systems in South Asia (TAFSSA) initiatives in collaboration with Bangladesh Dept. of Agricultural Extension (DAE) and Bangladesh Meteorological Department (BMD).
Libro
CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA CLIMATE SERVICES LIVESTOCK DIGITAL TECHNOLOGY
Calibrated multi-model ensemble seasonal prediction of Bangladesh summer monsoon rainfall
Nachiketa Acharya Carlo Montes Timothy Joseph Krupnik (2023)
Bangladesh summer monsoon rainfall (BSMR), typically from June through September (JJAS), represents the main source of water for multiple sectors. However, its high spatial and interannual variability makes the seasonal prediction of BSMR crucial for building resilience to natural disasters and for food security in a climate-risk-prone country. This study describes the development and implementation of an objective system for the seasonal forecasting of BSMR, recently adopted by the Bangladesh Meteorological Department (BMD). The approach is based on the use of a calibrated multi-model ensemble (CMME) of seven state-of-the-art general circulation models (GCMs) from the North American Multi-Model Ensemble project. The lead-1 (initial conditions of May for forecasting JJAS total rainfall) hindcasts (spanning 1982–2010) and forecasts (spanning 2011–2018) of seasonal total rainfall for the JJAS season from these seven GCMs were used. A canonical correlation analysis (CCA) regression is used to calibrate the raw GCMs outputs against observations, which are then combined with equal weight to generate final CMME predictions. Results show, compared to individual calibrated GCMs and uncalibrated MME, that the CCA-based calibration generates significant improvements over individual raw GCM in terms of the magnitude of systematic errors, Spearman's correlation coefficients, and generalised discrimination scores over most of Bangladesh areas, especially in the northern part of the country. Since October 2019, the BMD has been issuing real-time seasonal rainfall forecasts using this new forecast system.
Artículo
Multi-Model Ensemble Seasonal Forecasting CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA CLIMATE SERVICES FORECASTING MONSOONS
Peerzadi Rumana Hossain T.S Amjath-Babu Timothy Joseph Krupnik (2023)
Very little research has focused on climate impacts on aquaculture and the potential of climate information services (CIS) for aquaculture to support sustainable development goals 2030 (SDGs)1. This study represents an effort to bridge this gap by conducting a first ex-ante economic evaluation of CIS for aquaculture in Bangladesh by semi-automating the extraction of data on climate-induced fish losses during 2011 to 2021 from popular online newspaper articles and corroborating them with available government and satellite datasets. During this period, Bangladesh faced an estimated loss of around 140 million USD for hatcheries, open water fish and shrimp. When validated with a year of country-wide official data on climate-induced economic losses to aquaculture, the damage reported from these media sources is approximately 10 percent of actual losses. Given this rule of thumb, the potential economic value of aquacultural CIS could be up to USD14 million a year, if 10 percent of the damage can be offset by appropriate services through a range of multi-sector efforts to establish and extend these services to farmers at scale.
Artículo
Climate Information Services Newspaper Scraping CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA LOSSES AQUACULTURE CLIMATE SERVICES SUSTAINABLE DEVELOPMENT GOALS
Jelle Van Loon Timothy Joseph Krupnik Jesús A. López-Gómez Jagadish Timsina Bram Govaerts (2023)
Development of a methodology for mechanical maize meter evaluation that compares diverse meters in terms of seed singulation, seed damage, and spatial distribution
Dataset
Ernesto Adair Zepeda Villarreal T.S Amjath-Babu Sharif Ahmed Humnath Bhandari Mahesh Gathala Timothy Joseph Krupnik Santiago Lopez-Ridaura (2024)
Dataset processed from a household-level survey to describe the main farm characteristics, production, and resource allocation in two municipalities across three regions of Bangladesh: North (Dinajpur, Nilphamari, Rangpur), West (Nawabganj, Rajshahi), and South (Barguna, Barisal, Patuakhali). Data was collected between December 2022 and June 2023.
Dataset
Production vulnerability to wheat blast disease under climate change
Diego Pequeno Jose Mauricio Fernandes Pawan Singh Willingthon Pavan Kai Sonder Richard Robertson Timothy Joseph Krupnik Olaf Erenstein Senthold Asseng (2024)
Artículo
Wheat Blast Tropical Regions CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA WHEAT PLANT DISEASES CLIMATE CHANGE PRODUCTION