Benchmarking Cost of Milk Production in 46 Countries

The global dairy industry is facing challenges due to the extremely volatile milk price and a substantial increase of feed prices. The goal of this study, therefore, was to compare and benchmark the cost of milk production in 46 countries representing 87% of the world's total milk production, using a standard method developed by the International Farm Comparison Network (IFCN). Two typical farms were selected per country; one average-sized and one larger farm. The cost of milk production in 2010 ranged from 16.91US-$/100kg Energy Corrected Milk (ECM) in Armenia to 97.27 US-$/100kg ECM in Switzerland, with cost differences mainly driven by the diversity in farming and feeding systems. Based on costs, world regions were categorized into four levels: 40-50 US-$in the EU, Middle East and China; 30-40 US-$ in the USA, Brazil, CEEC and Oceania; <30 US- in Africa, Asia, South America; >60 US- in Austria, Norway, Switzerland and Canada. The major drivers for this variation were ranked as; purchased feed cost (the highest) followed by labor, land and machinery costs. Regression analyzes showed that costs were highly correlated milk yield and milk price but not to herd size

Effects of politically controlled boards on bank loan performance: an emerging economy perspective

Purpose – In this study, the authors explore the effects of politically controlled boards on bank loan performance in both state-owned commercial banks (SCBs) and private sector commercial banks (PCBs) in Bangladesh.
 Design/methodology/approach –The data consist of 409 bank-year observations from 46 sample SCBs and PCBs of Bangladesh for the period 2008–17. The authors apply ordinary least squares pooled regression with year fixed effect for baseline econometric analyses and generalized method of moments regression for robustness tests after addressing the endogeneity issue.
 Findings – The regression results reveal that the presence of bank “boards controlled by politically affiliated directors” (PA) have significant positive effects on non-performing loans (NPLs). Similarly, the presence of “boards controlled by politically affiliated directors without substantial ownership interests” (PAWOI) show positive association with NPLs. In contrast, the presence of “boards controlled by politically affiliated directors with substantial ownership interests” (PAOI) exhibit an inverse relationship with NPLs. These findings support ‘agency conflict’ arguments and document that both PA and PAWOI are detrimental to bank loan performance in Bangladesh, while PAOI do not have significant effect on increasing NPLs.
 Originality/value – This study contributes to the existing bank governance literature by providing evidence from an emerging economy perspective, where politically affiliated directors (PADs) exploit their positions for personal and/or political gain at the cost of other stakeholders by taking advantage of relaxed regulatory oversights and investor protections

Biorthogonal wavelet based entropy feature extraction for identification of maize leaf diseases

Crop disease is considered as a major constraint to both food quality and production. Even in this era of precision agriculture, the lacking of compulsory infrastructure has made rapid identification of crop diseases quite hard in numerous regions around the world. In this paper, we introduced a new method based on biorthogonal wavelet transform (BWT) to identify prime maize leaf diseases. We performed biorthogonal wavelet decomposition and pixel wise morphological operation to segment the maize leaf lesion from input image. For feature extraction, by applying 2-D biorthogonal wavelet transform (BWT) at multiple levels we proposed a novel method to extract colour channel wise wavelet entropy features by investigating discriminatory potential of three different biorthogonal wavelet filters (bior3.3, bior3.5, and bior3.7). The effectiveness of our extracted features were evaluated by employing five different classifiers and obtaining 95.78% overall identification accuracy with 10-fold cross validation. All the materials related our study can be found at: https://github.com/BadhanMazumder/BiorthogonalWavelet_MaizeDiseaseDetection.git

Determination of consumer milk price in the informal dairy market in Bangladesh: A district level analysis of vertical system linkage

The objective of this study was to identify the determinants of consumer milk prices and test the hypothesis that input prices, e.g., rice straw and wheat bran, as well as the farmgate milk price exert an influence on the consumer milk price. A unique panel dataset from July 2018 to June 2021 was obtained from the Integrated Dairy Research Network (IDRN) Bangladesh Monthly Dairy Sector database and was analysed using the Generalized Methods of Moments (GMM) methodology. The data collection and validation were done with a national panel of experts jointly with the data collection and processing team. The study found: a) vertical linkage with input and output price is highly affecting the consumers milk price which was due to the substantial variation across the regions and time; b) The dynamic panel analysis of GMM revealed mixed relationship between input prices (rice straw and wheat bran), farm gate milk prices, and the consumer milk price; c) The farmgate milk price determines the increase in consumer milk prices, and it is possible to predict the consumer milk price based on the time-price-system interaction variability of the farmgate milk price; d) Using COVID-19 as a proxy for real time impact, the study found a stark impact of COVID-19 on the input price and output prices and triggering a decrease in consumer milk prices by 3.96 BDT kg-1 milk (0.05 USD kg-1). The findings of this study are expected to be beneficial to the decisions making process of dairy farmers, milk processors, feed industry, consumers, and policy makers

Machine learning-based diagnosis of breast cancer utilizing feature optimization technique

Breast cancer disease is recognized as one of the leading causes of death in women worldwide after lung cancer. Breast cancer refers to a malignant neoplasm that develops from breast cells. Developed and less developed countries both are suffering from this extensive cancer. This cancer can be recuperated if it is detected at an early stage. Many researchers have proposed several machine learning techniques to predict breast cancer with the highest accuracy in the past years. In this research work, the Wisconsin Breast Cancer Dataset (WBCD) has been used as a training set from the UCI machine learning repository to compare the performance of the various machine learning techniques. Different kinds of machine learning classifiers such as support vector machine (SVM), Random Forest (RF), K-nearest neighbors(K-NN), Decision tree (DT), Naïve Bayes (NB), Logistic Regression (LR), AdaBoost (AB), Gradient Boosting (GB), Multi-layer perceptron (MLP), Nearest Cluster Classifier (NCC), and voting classifier (VC) have been used for comparing and analyzing breast cancer into benign and malignant tumors. Various matrices such as error rate, Accuracy, Precision, F1-score, and recall have been implemented to measure the model's performance. Each Algorithm's accuracy has been ascertained for finding the best suitable one. Based on the analysis, the result shows that the Voting classifier has the highest accuracy, which is 98.77%, with the lowest error rate. Finally, a web page is developed using a flask micro-framework integrating the best model using react

Institutional and organizational issues in livestock services delivery in Bangladesh

The objective of this paper is to explore the issue of artificial insemination (AI) services delivery in relation to livestock development by reviewing to the case of institutional and organizational arrangement in Bangladesh. It is argued that globalization and adaptation of open market economy policy has opened the significant opportunities for livestock keepers. The demand for livestock would increase almost two folds due to increase in population, rapid urbanization, and rise in income and would continue to rise till 2020. This increase in demand will create a potential market opportunities. The ability of the farmers to exploit those opportunities is linked critically to the availability and access to quality AI services. This paper is an attempt to provide the background information of AI starting from historical context to the existing institutional arrangement and synthesis of institutional framework for providing the AI services effectively and efficiently in Bangladesh