Field-level Data Research Articles

Watershed-scale spatial prediction of agricultural land phosphorus mass balance and soil phosphorus metrics: A bottom-up approach.

Analysis of nutrient balance at the watershed scale, including for phosphorus (P), is typically accomplished using aggregate input datasets, resulting in an inability to capture the variability of P status across the study region. This study presents a set of methods to predict and visualize partial P mass balance, soil P saturation ratio (PSR), and soil test P for agricultural parcels across a watershed in the Lake Champlain Basin (Vermont, USA) using granular, field-level data. K-means cluster analyses were used to group agricultural parcels by soil texture, average slope, and crop type. Using a set of parcels accounting for ∼21% of the watershed's agricultural land and having known soil test and nutrient management parameters, predictions of partial P mass balance, PSR, and soil test P for agricultural land across the watershed were made by cluster, incorporating uncertainty. This resulted in an average partial P balance of 5.5±0.2kg P ha-1year-1 and an average PSR of 0.0399±0.0002. Furthermore, approximately 30% of agricultural land had predicted soil test P values above optimum levels. Results were used to visualize areas with high P loss potential. Such data and visualizations can inform watershed P modeling and assist practitioners in nutrient management decision making. These techniques can also serve as a framework for bottom-up modeling of nutrient mass balance and soil metrics in other regions.

Subfield-level crop yield mapping without ground truth data: A scale transfer framework

Ongoing advances in satellite remote sensing data and machine learning methods have enabled crop yield estimation at various spatial and temporal resolutions. While yield mapping at broader scales (e.g., state or county level) has become common, mapping at finer scales (e.g., field or subfield) has been limited by the lack of ground truth data for model training and evaluation. Here we present a scale transfer framework, named Quantile loss Domain Adversarial Neural Networks (QDANN), that leverages knowledge from county-level datasets to map crop yields at the subfield level. Based on the strategy of unsupervised domain adaptation, QDANN is trained on labeled county-level data and unlabeled subfield-level data, with no requirement for yield information at the subfield level. We evaluate the proposed method applied to Landsat imagery and Gridmet weather data for maize, soybean, and winter wheat fields in the United States, using as reference data yield monitor records from roughly one million field-year observations. The model is compared with several process-based and machine learning-based benchmark approaches that train on simulated yield records or county-level data. QDANN-estimated yields achieved an R2 score (RMSE) of 48 % (2.29 t/ha), 32 % (0.85 t/ha), and 39 % (1.40 t/ha) for maize, soybean, and winter wheat in comparison with the ground-based yield measures, respectively. These performances are higher than benchmark approaches and are nearly as good as models trained on field-level data. When aggregated to the county level, the improvement achieved by QDANN is more pronounced and the R2 scores (RMSE) improved to 78 % (0.98 t/ha), 62 % (0.37 t/ha), and 53 % (1.00 t/ha) for maize, soybean, and winter wheat, respectively. This study demonstrates that the proposed scale transfer framework can serve as a reliable approach for yield mapping at the subfield level when there is no access to fine-scale yield information. Based on the QDANN model, we have generated and made publicly available 30-m annual yield maps for major crop-producing states in the U.S. since 2008.

Maintaining environmental context and geoprivacy protection in agriculture

To achieve sustainable agriculture and food security there is an urgent need to share agricultural data with a range of relevant stakeholders; however, to reduce the risk of identification, spatial data must be obfuscated prior to sharing. To-date, most obfuscation methods that have been developed do not consider a) the areal nature of field-level data and b) the differing environmental conditions at the original and obfuscated sites. To address these issues, we developed the Polygon-based Environmental Similarity Obfuscation Method (PESOM) to provide geoprivacy protection and guarantee that obfuscated data will retain the same environmental conditions as the original data. PESOM was developed using an unsupervised clustering algorithm and seasonal climate data, before being applied to the Nutrient Management Plan (NMP) online in Ireland. PESOM satisfied high level of geoprivacy protection and absolute environmental clustering preservation, with no false-identification and non-unique obfuscation risk. It provided a low level of distribution preservation and correlation preservation, large location displacement and subsequently low local analytical accuracy. PESOM is a significant advance on existing obfuscation techniques in agriculture data and will allow the sharing of data to be used widely for agri-environmental purposes, a current limitation of existing methods. The results of this research should be of wide interest to those working in agri-environmental research and computer science, and be of relevance to researchers, data managers, and practitioners.

Farming system archetypes help explain the uptake of agri-environment practices in Europe

The adoption of agri-environment practices (AEPs) is crucial for safeguarding the long-term sustainability of ecosystem services within European agricultural landscapes. However, the tailoring of agri-environment policies to the unique characteristics of farming systems is a challenging task, often neglecting local farm parameters or requiring extensive farm survey data. Here, we develop a simplified typology of farming system archetypes (FSAs), using field-level data on farms’ economic size and specialisation derived from the Integrated Administration and Control System in three case studies in Germany, Czechia and the United Kingdom. Our typology identifies groups of farms that are assumed to react similarly to agricultural policy measures, bridging the gap between efforts to understand individual farm behaviour and broad agri-environmental typologies. We assess the usefulness of our approach by quantifying the spatial association of identified archetypes of farming systems with ecologically relevant AEPs (cover crops, fallow, organic farming, grassland maintenance, vegetation buffers, conversion of cropland to grassland and forest) to understand the rates of AEP adoption by different types of farms. Our results show that of the 20 archetypes, economically large farms specialised in general cropping dominate the agricultural land in all case studies, covering 56% to 85% of the total agricultural area. Despite regional differences, we found consistent trends in AEP adoption across diverse contexts. Economically large farms and those specialising in grazing livestock were more likely to adopt AEPs, with economically larger farms demonstrating a proclivity for a wider range of measures. In contrast, economically smaller farms usually focused on a narrower spectrum of AEPs and, together with farms with an economic value <2 000 EUR, accounted for 70% of all farms with no AEP uptake. These insights indicate the potential of the FSA typology as a framework to infer key patterns of AEP adoption, thus providing relevant information to policy-makers for more direct identification of policy target groups and ultimately for developing more tailored agri-environment policies.

The cost of mismanaging crop heat stress with irrigation: Evidence from the mid-south USA

Field level data from a voluntary water use reporting program in the Delta region of Mississippi (MS), USA, provides empirical evidence that crop growers increase the amount of groundwater pumped for irrigation during periods of high temperature (degree days above 32 C). Regression analysis reveals that growers apply excess irrigation to cope with high temperature conditions while growing season evapotranspiration and precipitation are not significant factors in their decision of how much irrigation to apply. The existing literature indicates that excess water does not alleviate the harmful effect of extreme heat, making the excess irrigation wasteful and costly. Additional degree days above 32 C are associated with a 4.6 mm increase in irrigation depth (46 m3ha−1). This result may be partially explained by the fact that most farmers use visual cues (such as leaf folding which correlate with air temperature) to initiate irrigation events regardless of soil moisture contents. To quantify the impact of this mismatch, we estimate that shifting the underlying sensitivity in the farmers’ minds by 1 C would reduce groundwater pumpage by 800862 mega liters across the Delta region of Mississippi, USA. The monetary cost of mismanaging heat stress with excessive irrigation is estimated at a minimum of over US$33 million per year. We further identify two additional possible distortions affecting the demand for irrigation groundwater: (i) overestimated benefits from irrigation; and (ii) underestimated groundwater pumping cost. The policy implication is that more efforts and incentives should be placed for programs that affect the producers’ water management mindset, such as "Master Irrigator" programs that will reduce or eliminate the identified groundwater distortions and optimize benefits from irrigation.

Towards comprehensive urban sustainability: navigating predominant urban challenges and assessing their severity differential in Bangladeshi city corporations

Purpose Despite city authorities in Bangladesh being concerned about urban sustainability, they often face difficulties in addressing predominant urban challenges threatening urban sustainability, due to limited relevant literature. To reduce this gap, this study aims to address the predominant urban challenges and assess their severity levels in four city corporations of Bangladesh, e.g. Rajshahi, Sylhet, Barishal, and Gazipur. Design/methodology/approach Using a mixed-method approach, this study rigorously analyzed field-level data obtained from 1,200 residents across selected cities using diverse statistical techniques. The quantitative analysis included descriptive analysis, exploratory factor analysis, and chi-square tests, whereas qualitative insights were derived through thematic analysis. Findings The study uncovered nine predominant urban challenges under two crucial factors “Feeble Urban Management” and “Illicit Activities” that collectively explain 62.20% variance. “Feeble Urban Management” explains 44.17% variance, whereas “Illicit Activities” accounts for 18.13%. Within these challenges, uncontrolled urban sprawl, inadequate disaster management, congested roads, and shabby drainage and waste management pose significant threats to urban sustainability. Illicit activities, manifested by encroachment on water sources, grabbing roadside, destruction of natural properties, and activities undermining social security, compound the urban sustainability issue. Severity analysis reveals Sylhet (54.5%), Rajshahi (46.4%), and Barishal (31.2%) as highly impacted, whereas Gazipur exhibits moderate severity (66.7%). Originality/value The findings of this study reveal intrinsic insights into urban challenges in Bangladesh that will provide valuable guidance to city authorities, equipping them to implement integrated and effective initiatives and programs that overcome these predominant urban challenges, with a specific focus on Rajshahi, Sylhet, and Barishal city corporations.

Safeguarding Citizen Security and Fostering Economic Prosperity in Bangladesh through Digitization

"In recent years, Bangladesh has witnessed a transformative shift towards digitization as a key driver for enhancing citizen security and fostering economic prosperity. This abstract explores the multifaceted impact of digitization on the socio-economic landscape of Bangladesh and how it contributes to safeguarding citizen security while catalyzing economic growth. The study used first-hand information. Face-to-face interviews with members of the general public at different levels were used to gather primary data on the ground. In order to identify the driving forces behind citizen security, the gathered data were edited, coded, and evaluated. Descriptive statistical methods and tools were used in the study to conduct the analysis on the data sets and generate the results. According to an analysis of the survey’s field level data, 92 percent of respondents believe that security is important for creating a digital Bangladesh that protects its citizens. The majority of locals (85%) are unaware of digital security. 85% of residents are unaware about digital security. The majority of rural communities lack adequate knowledge of digital schooling. The building of a digital Bangladesh will be extremely simple and the protection of all deserving residents of the nation will be guaranteed if the development of digital education is feasible. The digitization initiatives in Bangladesh encompass various sectors, including governance, finance, healthcare, and education. The implementation of digital governance has streamlined administrative processes, reducing bureaucratic bottlenecks and enhancing the efficiency of public services. This not only ensures a more transparent and accountable government but also strengthens the security of citizens’ personal data. The financial sector has witnessed a paradigm shift with the widespread adoption of digital payment systems and mobile banking. This has not only provided financial inclusion to previously underserved populations but has also mitigated the risks associated with traditional cash transactions. The increased accessibility to financial services has empowered citizens and contributed to economic prosperity by fostering entrepreneurship and facilitating smoother business transactions. Furthermore, the digitization of healthcare services has improved accessibility and quality of medical care, especially in remote areas. Telemedicine and digital health records have played a pivotal role in connecting patients with healthcare professionals, ensuring timely and efficient healthcare delivery. This has not only safeguarded the health of citizens but has also contributed to overall economic productivity by reducing absenteeism due to health-related issues. In conclusion, the ongoing digitization initiatives in Bangladesh have emerged as a powerful catalyst for safeguarding citizen security and fostering economic prosperity. As the nation continues to embrace digital transformation, a strategic and inclusive approach is crucial to harness the full potential of digitization and create a resilient, secure, and prosperous future for its citizens."

The Economic Evaluation of Various Production Scenarios for Zubair Reservoir in the Kifl Field

This work evaluates the economic feasibility of various production scenarios for the Zubair reservoir in the Kifl oil field using cash flow and net present value (NPV) calculations. The Kifl field is an exploratory field that has not yet been developed or assessed economically. The first well was drilled in 1960, and three other wells were later drilled to assess the oil accumulation, so in this research, Different production scenarios were evaluated economically. These scenarios were proposed based on the reservoir model of the Zubair formation in the field. The research methodology used QUE$TOR software to estimate capital expenditures (CapEx) and operating expenditures (OpEx) based on field-level data, production profiles for each scenario, and produced fluid properties. CapEx estimates were mainly based on the number of drilled wells and plateau duration, while OpEx estimates were based on the number of barrels produced from each scenario. Cash flow values were estimated each year in the production interval after subtracting water handling costs, CapEx, and OpEx from the gross revenue of oil produced. The NPV for each scenario was then calculated using a 5% interest rate, and the most economical option was determined by comparing the NPV values of each scenario. Case-3 scenario was identified as the most economical option, with a high NPV value of approximately 612 MM$, an oil production rate of 10 MSTB/d, a Plateau duration of 3 years, a total number of wells of 11, and an Initial water cut of 13 %.

Exploration of Quantum Cryptography Security Applications for Industrial Control Systems

Abstract The exploration of security applications of quantum cryptography for industrial control systems is a key research effort aimed at enhancing the security of industrial control systems through quantum cryptography. In this paper, we study the security threats faced by industrial control systems, including network attacks, data leakage, and system tampering, and propose to utilize quantum key distribution and quantum invisible state transfer algorithms to ensure the secure transmission of industrial control system data. The simulation test environment of the upper and lower computers of the industrial control system is built. The quantum encryption and decryption algorithms are deployed in the embedded environment and PCs to test the effectiveness of quantum cryptography to enhance the advanced encryption standard key scheme. The experimental results show that the quantum cryptography technology successfully realizes the encryption and decryption of data, and the total time consumed in the whole process is less than 61.8 seconds, which meets the requirements of a real-time industrial control system. Therefore, quantum cryptography is suitable for protecting field-level data in industrial control systems.

Smart Public Transportation Sensing: Enhancing Perception and Data Management for Efficient and Safety Operations.

The use of cloud computing, big data, IoT, and mobile applications in the public transportation industry has resulted in the generation of vast and complex data, of which the large data volume and data variety have posed several obstacles to effective data sensing and processing with high efficiency in a real-time data-driven public transportation management system. To overcome the above-mentioned challenges and to guarantee optimal data availability for data sensing and processing in public transportation perception, a public transportation sensing platform is proposed to collect, integrate, and organize diverse data from different data sources. The proposed data perception platform connects multiple data systems and some edge intelligent perception devices to enable the collection of various types of data, including traveling information of passengers and transaction data of smart cards. To enable the efficient extraction of precise and detailed traveling behavior, an efficient field-level data lineage exploration method is proposed during logical plan generation and is integrated into the FlinkSQL system seamlessly. Furthermore, a row-level fine-grained permission control mechanism is adopted to support flexible data management. With these two techniques, the proposed data management system can support efficient data processing on large amounts of data and conducts comprehensive analysis and application of business data from numerous different sources to realize the value of the data with high data safety. Through operational testing in real environments, the proposed platform has proven highly efficient and effective in managing organizational operations, data assets, data life cycle, offline development, and backend administration over a large amount of various types of public transportation traffic data.

Inferring warm dark matter masses with deep learning

ABSTRACT We present a new suite of over 1500 cosmological N-body simulations with varied warm dark matter (WDM) models ranging from 2.5 to 30 keV. We use these simulations to train Convolutional Neural Networks (CNNs) to infer WDM particle masses from images of DM field data. Our fiducial setup can make accurate predictions of the WDM particle mass up to 7.5 keV with an uncertainty of ±0.5 keV at a 95 per cent confidence level from (25 h−1Mpc)2 maps. We vary the image resolution, simulation resolution, redshift, and cosmology of our fiducial setup to better understand how our model is making predictions. Using these variations, we find that our models are most dependent on simulation resolution, minimally dependent on image resolution, not systematically dependent on redshift, and robust to varied cosmologies. We also find that an important feature to distinguish between WDM models is present with a linear size between 100 and 200 h−1 kpc. We compare our fiducial model to one trained on the power spectrum alone and find that our field-level model can make two times more precise predictions and can make accurate predictions to two times as massive WDM particle masses when used on the same data. Overall, we find that the field-level data can be used to accurately differentiate between WDM models and contain more information than is captured by the power spectrum. This technique can be extended to more complex DM models and opens up new opportunities to explore alternative DM models in a cosmological environment.

DOES FARMERS' EDUCATION AFFECT THE PROFITABILITY OF SHRIMP FARMING? EVIDENCE FROM THE COASTAL REGION OF BANGLADESH

This study explores the impact of farmers' level of education on the profitability of shrimp farming. The authors have collected data from a sample of 200 shrimp farmers in three districts in the southwest region of Bangladesh, namely Khulna, Bagerhat, and Satkhira, using a proportionate random sampling technique. The shrimp farmers' education and productivity status are investigated through descriptive statistics. The authors estimated a Cobb-Douglas Production Function (CDPF) to examine the effect of farmers' level of education on the profitability of shrimp farming. Using a Tobit regression model, this study tried to investigate the association between the educational attainment of farmers and their degree of knowledge in farm management. The study finds that the average level of shrimp production in southwestern Bangladesh is 425 kg per hectare, resulting from the absence of modern farming methods and a deficiency in adequate farming knowledge. The activity budget equation illustrates that farmers with higher education levels exhibit greater profitability compared to their less educated counterparts. The analysis of efficiency factors of production (EFP) indicates that farmers with higher education levels exhibit greater efficiency than their counterparts with lower education levels. The break-even point (BEP) analysis reveals that the average production volume, price, and the gher size in the study area outstrip the BEP threshold, suggesting that shrimp farming in this region is economically feasible. The analysis of field-level data reveals a strong association between farmers' level of education and their profitability, suggesting that higher levels of education among farmers have a substantial positive impact on their financial gains. It is evident from the study findings that knowledge of farm management plays a significant role in shrimp productivity. Therefore, the degree of farm management knowledge is affected by the education of the farmers. Consequently, this research suggests augmenting the accessibility of contemporary technology-driven farming methods and effectively utilizing information via a training initiative targeting shrimp farmers. Additionally, this study recommends offering incentives or credit opportunities to newly participating educated farmers.

Gender Disparity in Crop Productivity and Input Usage: Evidence from an Empirical Study

The study aims at examining the disparity in crop productivity and input usage between male- and female-headed self-cultivating farm households in the coastal region of Odisha, India. Field-level data from 221 sample households are collected and analysed following a multi-stage sampling procedure. The findings reveal that the male-headed farms have both higher productivity and better use of inputs in realizing returns than the female-headed farms. Within the female-headed farms, the de facto ones have an edge over their de jure counterparts in both these respects. Since the study area is a disaster-prone area, adding further to the gender-constraints of female-headed farms in the use of inputs and realizing commensurate yield, the study suggests for measures to build capacity among women farmers to produce efficiently.

From the Shadows to the Spotlight: Unveiling Nepal's hidden kingdom of mushrooms and lichens through digitization

The digitization of herbarium collections has brought forth a transformative journey, transitioning Nepal's hidden kingdom of mushrooms and lichens from the shadows into the spotlight. Through a collaborative work within the framework of Global Biodiversity Information Facility's Biodiversity Information Fund for Asia (GBIF-BIFA), involving the herbaria (KATH: Nepal's National Herbarium and Plant Laboratories and TUCH: Natural History Museum, Tribhuvan University, Nepal), and the research institute, Global Institute for Interdisciplinary Studies (GIIS), a successful unveiling of Nepal's mycological treasures has been achieved through digital means. A comprehensive digitization effort has resulted in the complete digitization of 3,971 mushroom specimens and 2,462 lichen specimens, illuminating a wealth of information for researchers, citizen scientists, and the general public. GBIF and the online database maintained by Nepal's National Herbarium and Plant Laboratories, Department of Plant Resources, serve as the gateway to this work (KATH 2021). Prior to this work, the specimens resided in the shadows, lacking the recognition they deserved. Through meticulous collection management, sorting, curation, and labeling, their secrets were unveiled, and their stories brought to our fingertips. These previously obscured specimens now possess registered individual catalogue numbers, allowing the quantification of Nepal's fungal wealth within the participating institutions. This project serves as a testament to the vital role of capturing available field-level data, preserving specimens, and harnessing the power of digitization to showcase Nepal's mycological and lichenological wonders to a global audience. Meanwhile, it has also emphasized the significance of sharing this knowledge and fostering appreciation for the overlooked world of mushrooms and lichens.

Developing a CDY Model for Grapes and Experimentally Validating it with an Android App that Focuses on Agro-climatic and Disease Prevention Aspects

Introduction: Crop development and yield are both influenced by the weather. This study has developed and analytically resolved a general agro-climatic model for grapes. Methods: In the field of mathematical biology, researchers, professors, and academics will find this model useful. To create the final version of the model for yield prediction, the CDY model and asymptotic analyses have been performed. Climate, disease, and grape production have been taken into consideration as dependent characteristics during the model construction process. The frequency of infection, the occurrence of disease, seasonality, and the elimination of grape output throughout each harvest cycle have been viewed as distinct qualities. Moreover, the model has been examined, and field-level data have been used to estimate the parameters collected between 2016-2021 from the nearby villages of GRS and Theni. Results: A description of this model’s stability analysis has also been provided. An association has been determined between the numerical validity and stability of the given analytical solution analyses. In addition, the developed Android mobile app for grapes has been validated using the proposed model under the climatic scenario. Conclusion: It is advised to apply the created model to estimate grape yield based on the findings obtained. A useful technique for forecasting crop yield has thus been proposed in this study.

Sweet corn yield prediction using machine learning models and field-level data

The advent of modern technologies, acquisition of large amounts of crop management and weather data, and advances in computing are reshaping modern agriculture. These advancements have unlocked the power of data by providing valuable insights and more accurate yield predictions. This study utilizes a historic US sweet corn dataset to: (a) evaluate machine learning model performances on sweet corn yield prediction and (b) identify the most influential variables for crop yield predictions. The sweet corn data comprised field-level data for over a quarter-century period (1992–2018) from two primary commercial sweet corn production regions for processing, namely the Upper Midwest and the Pacific Northwest. Several machine learning models were trained to predict field-level sweet corn yield from 67 variables of crop genetics, management, weather, and soil factors. The random forest model outperformed all trained models with the lowest RMSE (3.29 Mt/ha) and the highest Pearson’s correlation coefficient (0.77) between predicted and observed yields. Variable importance plots revealed the top three most influential predictor variables as year (time), location (space), and seed source (genetics). Season long total precipitation and average minimum temperature during anthesis were the two most important weather variables in yield prediction. This is the first report of using fine-scale (time and space) crop data and advanced data analytics to leverage insights into commercial sweet corn production.

Dupchanchia Model of Students’ drop-out control through engagement and appreciation after Covid-19: A Behavioral Policy intervention in the field administration of Bangladesh

Bangladesh is a country which has successfully accomplished the millennium development goals. Upon such accomplishment and with new growing consensus with the global community, the country at present is in pursuit of achieving the sustainable development goals with mostly concentrating on the education sector. However, what impacted the growth and pace of the initiatives was the overwhelming impact of Covid-19 and the lockdown afterwards. Academic institutions remained closed at least for two years which resulted in a compromising number of students after the resumption. This study starts from the identification of a genuine problem with original field level data of the gradually declining number of students. It requires policy intervention centrally and locally. As a crucial part of local government, I have been working as the chief executive officer of a sub-district called Dupchanchia, and coordinating government departments to implement government policy. After rigorous discussion and brainstorming among the local stakeholders and teachers we uncovered that the students have become demotivated, traumatized and panicked of social engagement and any form of shared activities like classes, games and others. It required us to find out a local policy solution followed by a detailed literature review and primary data collection maneuver. Taking twenty schools into consideration for the study, the project initiated a behavioral policy intervention in ten particular schools and did not interfere with the other ten schools. I engaged local teachers, students and other related stakeholders and continued to use six behavioral tools to change the choice structure of the students of ten selected schools. We observed other ten schools without intervening in their environment and academic atmosphere at all. At the end of the study we collected data through key informant interviews and focus group discussion engaging teachers, peoples’ representatives and government officials. Behavioral public policy intervention like nudge and engagement approaches are found to have a positive relation with the change in students number and their performance in the academic and co-curricular activities. This approach may contribute to controlling students' drop out in the lower and lower middle income countries after Covid shock. This policy intervention may have some challenges and limitations which need intensive and rigorous pre-study and prolonged design. Nevertheless, it has unlimited opportunities to be addressed.

Farmers’ Perception and Factors Affecting Adoption Of Mungbean in Patuakhali District of Bangladesh

Mungbean is an important pulse crop in Bangladesh for nutrition, the economy, and food security, as well as a source of protein for the majority of the people. The study was carried out to better understand farmers' views toward mungbean production, the factors affecting mungbean adoption, and the financial profitability of mungbean cultivation. A structured and pre-tested interview schedule was used to interview 90 mungbean farmers that were randomly selected from different villages in Patuakhali Sadar Upazila, Patuakhali district for collecting field level data. A combination of descriptive, mathematical and statistical techniques was used to analyze the data. Profitability analysis showed that mungbean production was profitable because the net return of its cultivation was Tk. 21,959/ha and BCR was more than unity (1.73). The study revealed that short duration crop, sharing work within household members, crop diversification, minimum tillage, employment creation, and income generation significantly influenced farmers to cultivate mungbean than any other crop. Problem facing index pointed out high price of seed and fertilizer, lack of good quality seed and fertilizer, inadequate extension service as a production related problem and lack of value added product development and transport facility, low market price of output and lack of storage facilities were identified as a major marketing problem of mungbean cultivation.&#x0D; Bangladesh J. Agril. Res. 46(4): 385-396, December 2021

Market attractiveness of pineapple and banana agroforestry systems of Madhupur Sal (Shorea robusta) forest: A sustainable way of generating income and conserving forests

The agroforestry system is an important land and resource management system for the socio-economic development of forest-dependent people. We investigate the farming attractiveness and strength of crop cultivation in the agroforestry system by applying the McKinsey analytical tool. Field-level data were collected through Focus Group Discussions (FGDs). The findings show that participants are young and educated, received training, and have large farming experience. Participants strongly agreed on building trust with buyers and disagreed on marketing communication for selling their produce. The McKinsey matrix analysis shows that pineapple (Ananas comosus) and banana (Musa oranta) are leading fruits cultivated in the agroforestry system, with pineapple receiving higher attention compared with banana due to greater market potential. Policies should be implemented to remove the barriers that limit pineapple and banana production and marketing and establish communications between buyers and farmers to sustain production.

Developing Prediction and Forecasting modelfor coconut under weather parameters and external factors in Coimbatore district.

Coconut is the most important plantation crop, planted in nearly every country. Coimbatore is the major producer of coconut in Tamil Nadu, followed by Thanjavur and Kanyakumari, hence this research is focused on the Coimbatore district. West Coast Tall is the popular variety which gives better yield than other varieties. This study was carried out on West Coast Tall (WCT) variety. In this paper, Coconut yield prediction model is developed under weather and external factors such as Minimum temperature, Relative Humidity, Rainfall, Plant height, Stem girth, Female flowers in inflorescence, Leaf length and Copra content. Correlation and multiple regression analysis is carried out to obtain the final form of the yield prediction model. Further, we validate this model using field-level data from TNAU coconut research farm using MATLAB and python software. The actual yield in comparison with the predicted yield using software is found to be in satisfactory agreement. In addition, we developed forecasting model for coconut for forthcoming years using proposed model.