Agriculture is an important source of greenhouse gas emissions. It is one of the economic sectors that impacts both directly and indirectly towards climate change which contributes to greenhouse gas emissions. There has been a continuous trend of agricultural greenhouse gas emissions reduction technologies, but any step taken in this direction must not negatively affect farm productivity and economics. For the agriculture sector to achieve reduced GHG emission, climate-smart activities and improved food security will be needed for this sector to become a climate-smart landscape. Climate-smart technologies are effective at targeting inputs to the fields, helping to lower greenhouse gas emissions. This article explores the key sources of carbon emissions within the agriculture sector and reviews efficient ways to GHG emission via Smart Farming technology. Based on the public archive GHG datasets, we have found that livestock farming is the largest GHG emission sector among other agricultural sectors and responsible for $70\%$ of the total emission. Besides, we also show that Queensland is the largest agricultural GHG contributor compared to other states and territories. The article also captures any possible sources within smart farming that may contribute to carbon emissions and suggest ways to reduce GHG emissions. Besides, an Australian-based best management practice approach is discussed to review the emissions reduction strategy based on climate-specific technology to help the farmers and other stakeholders take environmentally-friendly agricultural decisions.

Smart farming has the potential to overcome the challenge of 2050 to feed 10 billion people. Both artificial intelligence (AI) and the internet of things (IoT) have become critical prerequisites to smart farming due to their high interoperability, sensors, and cutting-edge technologies. Extending the role of responsible leadership, this paper proposes an AI and IoT based smart farming system in Bangladesh. With a comprehensive literature review, this paper counsels the need to go beyond the simple application of traditional farming and irrigation practices and recommends implementing smart farming enabling responsible leadership to uphold sustainable agriculture. It contributes to the current literature of smart farming in several ways. First, this paper helps to understand the prospect and challenges of both AI and IoT and the requirement of smart farming in a nonwestern context. Second, it clarifies the interventions of responsible leadership into Bangladesh’s agriculture sector and justifies the demand for sustainable smart farming. Third, this paper is a step forward to explore future empirical studies for the effective and efficient use of AI and IoT to adopt smart farming. Finally, this paper will help policymakers to take responsible initiatives to plan and apply smart farming in a developing economy like Bangladesh.

Potato (Solanum tuberosum L.) is one of the most significant vegetable crops grown globally, especially in developing countries. Over the last few years, global potato production has been increasing. This growth has created many opportunities for developing a wide range of value-added products from these crops. However, this requires monitoring the quality components of the tubers, such as moisture content, starch content, and soluble solid content. In particular, moisture content is one of the key quality parameters important for ensuring quality control throughout the supply chain and processing for consumer consumption. Ideally, moisture content would be estimated at the field level; however, current methods used by the industry to assess moisture content are time-consuming, labor-intensive, and destructive. Hence, the purpose of this study is to investigate the feasibility of hyperspectral imaging to quantify the moisture content of unpeeled potatoes before they were subsequently stored and processed. Hyperspectral images are collected from 47 intact potato tubers, with partial least squares regression (PLSR) models developed to predict moisture content from these spectra. The models showed predictive abilities for moisture content with acceptable ratios of prediction to deviation (RPDs) when considering the complete wavelength range (R² = 0.53, RPD = 1.46, root mean square error (RMSE) = 5.04%) or the β-coefficient wavelength selection technique (R² = 0.53, RPD = 1.47, RMSE = 5.02%). Furthermore, the prediction ability increased by more than 10% when the model wavelength was narrowed down to 733–970 nm. This study demonstrates the potential of using hyperspectral imaging for the quality assessment of intact, unpeeled potatoes, although further work is required to improve the model quality and implement this approach using remote sensing imagery.

Hill forest vegetation cover in the southeastern part of Bangladesh has been facing degradation and depletion over a few decades. The forest estimation and mapping of this country are well documented and mainly restricted to the mangrove extent. However, the monitoring of the hill forest vegetation of Bangladesh is limited till now. This study monitor and analyzes the forest vegetation cover changes using Landsat imagery from 1974 to 2020, specifically in Khagrachari and Rangamati hill district, Bangladesh. We preprocess the satellite imagery and then perform a decision tree classification algorithm based on the spectral indexes derived from the imagery. The initial assessment indicates the negative change of dense vegetation/forest vegetation cover (FVC) in most parts of the study area since 1974. Further results show that ~57.17%, ~39.3%, ~31.27%, and ~24.97% of the total area were classified as FVC type in 1989,1999, 2010 and 2020, respectively. Besides, this study briefly discusses how the change of FVC impacts the life of the local indigenous community living around the area. This preliminary investigation highlights deforestation over the ~46 years around the study area, which could be beneficial for planning to manage and conserve forest resources, and protect the local indigenous community.

Radiance observations are typically affected by biases that come mainly from instrument error (scanning or calibration) and inaccuracies of the radiative transfer model. These biases need to be removed for successful assimilation, so a bias correction scheme is crucial in the Numerical Weather Prediction (NWP) system. Today, most NWP centres, including the Bureau of Meteorology (hereafter, “the Bureau”), correct the biases through variational bias correction (VarBC) schemes, which were originally developed for global models. However, there are difficulties in estimating the biases in a limited-area model (LAM) domain. As a result, the Bureau’s regional NWP system, ACCESS-C (Australian Community Climate and Earth System Simulator-City), uses variational bias coefficients obtained directly from its global NWP system ACCESS-G (Global). This study investigates independent radiance bias correction in the data assimilation system for ACCESS-C. We assessed the impact of using independent bias correction for the LAM compared with the operational bias coefficients derived in ACCESS-G between February and April 2020. The results from our experiment show no significant difference between the control and test, suggesting a neutral impact on the forecast. Our findings point out that the VarBC-LAM strategy should be further explored with different settings of predictors and adaptivity for a more extended period and over additional domains.