To study the environment of the Kipushi mining locality (LMK), the evolution of its landscape was observed using Landsat images from 2000 to 2020. The evolution of the landscape was generally modified by the unplanned expansion of human settlements, agricultural areas, associated with the increase in firewood collection, carbonization, and exploitation of quarry materials. The problem is that this area has never benefited from change detection studies and the LMK area is very heterogeneous. The objective of the study is to evaluate the performance of classification algorithms and apply change detection to highlight the degradation of the LMK. The first approach concerned the classifications based on the stacking of the analyzed Landsat image bands of 2000 and 2020. And the second method performed the classifications on neo-images derived from concatenations of the spectral indices: Normalized Difference Vegetation Index (NDVI), Normalized Difference Building Index (NDBI) and Normalized Difference Water Index (NDWI). In both cases, the study comparatively examined the performance of five variants of classification algorithms, namely, Maximum Likelihood (ML), Minimum Distance (MD), Neural Network (NN), Parallelepiped (Para) and Spectral Angle Mapper (SAM). The results of the controlled classifications on the stacking of Landsat image bands from 2000 and 2020 were less consistent than those obtained with the index concatenation approach. The Para and DM classification algorithms were less efficient. With their respective Kappa scores ranging from 0.27 (2000 image) to 0.43 (2020 image) for Para and from 0.64 (2000 image) to 0.84 (2020 image) for DM. The results of the SAM classifier were satisfactory for the Kappa score of 0.83 (2000) and 0.88 (2020). The ML and NN were more suitable for the study area. Their respective Kappa scores ranged between 0.91 (image 2000) and 0.99 (image 2020) for the LM algorithm and between 0.95 (image 2000) and 0.96 (image 2020) for the NN algorithm.

Smallholder cocoa producers often experience low productivity levels, partly due to their weak collaborative advantage (CA). CA enables businesses to optimize outcomes through effective collaboration within value chains. This paper aims at examining the effect of CA pillars (trust building, resource investment, and decision synchronization) on the productivity. This paper uses primary data of 406 samples from smallholder cocoa producers in Indonesia. The data is analyzed by using CDM (Crepon Duguet Mairesse) model that divides the CA process into three stages: effort, output, and productivity. In the first stage, our model shows that having motivation to collaborate positively affects collaborative effort expenditure to develop a CA. In the second stage, the study finds that the three pillars of CA have to some degree contributes to achieving a better access to finance, superior cocoa seeds, and cocoa processing technology for smallholder cocoa producers. In the third stage, acquiring the outputs of CA leads to productivity improvement. The findings underscore the significance of intangible factors in shaping robust Collaborative Advantage (CA) and influencing productivity. This enriches CA theory, which has traditionally focused primarily on tangible factors.

A decent income is an important part of overcoming economic disparities in agricultural development, especially in developing countries where most of the population are small farmers. As a developing country, Indonesia has also established a decent standard of living by setting a minimum wage as a reference for a decent income at the national and regional levels. However, this benchmark is not relevant to be applied uniformly at all levels of workers. This research determines the national coffee development area as the study center. We developed the Anker living wage methodology as a simple concept for determining living income for certain worker communities, especially for small farmers in rural areas who dominate the type of work in Indonesia. a socio-spatial approach is used to visualize the distribution of the dynamics of a decent life in various conditions of farming households. We found that 96.6% of coffee farming households in the national coffee development area had an inadequate living income, and only 3.4% were at an adequate level. We conclude that the current state of agricultural land management does not guarantee a decent income, even though efforts have been made to maximize agricultural crop productivity. The spatial description also shows that this condition is evenly distributed throughout residential areas. It is hoped that this approach can become an essential reference in implementing agricultural development programs that focus on welfare and equitable development as benchmarks for sustainable development goals in the future.

The women’s sector in the academe is one of the most affected profiles during the COVID-19 pandemic which directly ravages their livelihood and other economic activities. Thus, this research project investigated the economic situations of 30 private and public-school teachers who were displaced from their occupations or were forcibly deprived of income-generating activities. In-depth interviews as research instruments were employed in the study to extract responses on how the educators creatively apply adaptive economic strategies and how government should aid them during a global crisis. The research findings showed that the pandemic has affected the economic activities of the respondents including the loss of their livelihood and other economic sidelines. They responded to these economic effects through adaptive strategies using diversifying and analyzing trends, using digital technology resources, data-driven, acquiring new alternative skills, pricing strategy, and becoming an expert. Results dictated that government could support affected women by initiating training options, homepreneurship support, encouraging independent income-earners, financial management and tax breaks, and industry compatibility endorsement. This study is important to map out the specific economic effects of the pandemic and aid them with initiatives by providing them with concrete economic tools and programs.

Endosulfan (6,7,8,9,10,10-Hexachloro-1,5,5a,6,9,9a-hexahydro-6,9-methano-2,4,3-benzodioxathiepine-3-oxide) is an off-patent insecticide used in agricultural farms. Its usage as a pesticide has become highly controversial over the last few decades. This is due to its reported hazardous nature to health and side effects like growth retardation, hydrocephalus, and undesired changes in the male and female hormones leading to complications in sexual maturity. Endosulfan is the main culprit among all pesticide poisoning incidents around the world. Though the usage of this dreaded pesticide is banned by most countries, the high stability of this molecule to withstand degradation for a long period poses a threat to mankind even today. So, it has become highly essential to detect the presence of this poisonous pesticide in the drinking water and milk around these places. It is also advisable to check the presence of this toxic material in the blood of the population living in and around these places so that an early and appropriate management strategy can be adopted. With this aim, we have developed a sensor for endosulfan that displayed high selectivity and sensitivity among all other common analytes in water and biological samples, with a wide linear concentration range (2 fM to 2 mM), a low detection limit (2 fM), and rapid response. A citrate-functionalized cadmium-selenium quantum dot was used for this purpose, which showed a concentration-dependent fluorescence enhancement, enabling easy and sensitive sensing. This sensor was utilized to detect endosulfan in different sources of water, human blood serum, and milk samples with good recoveries. It is also noted that the quantum dot forms a stable complex with endosulfan and is easy to separate from the contaminated source, paving the way for purifying the contaminated water. More detailed tests and validation of the sensor are needed to confirm these observations.