The unprecedented challenges presented by the increase in global population have placed substantial demands on the livestock industry for human nutrition, necessitating heightened animal productivity and leading to an increased demand for natural resources to produce animal feed. Feed producers are leading the charge, consistently refining formulations to adapt to the evolving needs of livestock, driven in part by the cost of over 50% associated with feed production. This paper critically analyses the pressing issues within feed formulation, addressing the requirement for environmentally sustainable practices amidst the challenges of climate change. The exploration extends to how advanced decision support tools can enhance formulation techniques and profitability and contribute to environmental sustainability. Through an in-depth review of current feed formulation technologies, encompassing their applications and limitations, this study aims to enhance the existing knowledge base. Additionally, we examined future trends, highlighting the essential role of connecting technologies to establish a resilient and sustainable system. The emphasis is on the potential of formulation techniques to positively impact the environment and enhance the overall quality and performance of the animals. This paper provides actionable insights to improve animal production by examining feed formulation models and decision support tools. The anticipated outcome is a more informed and sustainable decision-making process, addressing the multifaceted challenges confronted by the livestock industry and making contributions to global efforts in climate change mitigation and environmental stewardship in animal production agriculture.

The growing importance of rice globally over the past three decades is evident in its strategic place in many countries’ food security planning policies. Still, its cultivation emits substantial greenhouse gases (GHGs). The Indica and Japonica sub-species of Oryza sativa L. are mainly grown, with Indica holding the largest market share. The awareness, economics, and acceptability of Japonica rice in a food-insecure Indica rice-consuming population were surveyed. The impact of parboiling on Japonica rice was studied and the factors which most impacted stickiness were investigated through sensory and statistical analyses. A comparison of the growing climate and greenhouse gas emissions of Japonica and Indica rice was carried out by reviewing previous studies. Survey results indicated that non-adhesiveness and pleasant aroma were the most preferred properties. Parboiling treatment altered Japonica rice’s physical and chemical properties, introducing gelatinization of starch and reducing adhesiveness while retaining micronutrient concentrations. Regions with high food insecurity and high consumption of Indica rice were found to have suitable climatic conditions for growing Japonica rice. Adopting the higher-yielding, nutritious Japonica rice whose cultivation emits less GHG in these regions could help strengthen food security while reducing GHGs in global rice cultivation.

Protected cultivation systems such as greenhouses are becoming increasingly popular globally and have been adopted because of unpredictable climatic conditions and their ability to easily control micro- and macroenvironments. However, limitations such as hazardous work environments and shortages in labor are major concerns for agricultural production using these structures. This has led to the development and adoption of robotic systems. For the efficient use of robots in protected cultivation systems, we formulate the work efficiency problem and model a three-dimensional standard strawberry greenhouse to analyze the effectiveness of a strawberry-harvesting robot compared to different levels of human workforce (experienced, average, and beginner). Simulations are conducted using Quest software to compare the efficiency of different scenarios of robotics to humans. Different methods of improvement from battery capacity and charge rate to harvesting speed are investigated and optimal conditions are recommended. The average hourly production of the robot is about five times lower than that of skilled workers. However, robots are more productive due to their ability to work around the clock. Comparative analyses show that a reduction in harvesting time per strawberry from 3 to 1 s would result in an increase in daily production from 347.93 to 1021.30 kg. This would lead to a five-fold increase in comparison to present daily production. A 10% improvement in battery charge time would result in the battery capacity gaining two extra hours from the current 10 h and would cut the current 2 h needed for charge to 1 h. This paper proposes an operation process and suggestions for changes needed for improving the work efficiency of robots in a greenhouse. This could be extended to other crops and greenhouses.

The global demand for high-quality animal products, particularly dairy, has intensified the need for more precise and efficient livestock feed formulation. This review connects data-driven decision-making in optimizing feed formulation to enhance milk quantity and quality while addressing animal health implications. Modern feed formulation has evolved into a sophisticated, data-centric process by integrating diverse data sources such as nutritional databases, environmental data, and animal performance metrics. Leveraging advanced analytical techniques, such as machine learning and optimization algorithms, have created highly accurate feed formulations tailored to specific livestock needs. These innovations increase milk yield and contribute to developing dairy products with higher nutritional value. Decision Support Systems play a complementary role by offering real-time decision-making capabilities, enabling farmers to make data-informed adjustments composition based on changing conditions. However, despite its potential, the widespread adoption of data-driven feed formulation faces challenges such as data quality, technological limitations, and industry resistance, mostly disjointed processes. The objectives of this review are: (i) to explore the current advancements and challenges of data-driven decision-making in feed formulation, focusing on its connection to milk quantity and quality, and (ii) to highlight how this optimized feed formulation strategy improves sustainable dairy production.

Accurate measurement of micro-climates that include temperature and relative humidity is the bedrock of the control and management of plant life in protected cultivation systems. Hence, the use of a large number of sensors distributed within the greenhouse or mobile sensors that can be moved from one location to another has been proposed, which are both capital and labor-intensive. On the contrary, accurate measurement of micro-climates can be achieved through the identification of the optimal number of sensors and their optimal locations, whose measurements are representative of the micro-climate in the entire greenhouse. However, given the number of sensors, their optimal locations are proven to vary from time to time as the outdoor weather conditions change. Therefore, regularly shifting the sensors to their optimal locations with the change in outdoor conditions is cost-intensive and may not be appropriate. In this paper, a framework based on the dense neural network (DNN) is proposed to predict the measurements (temperature and humidity) corresponding to the optimal sensor locations, which vary relative to the outdoor weather, using the measurements from sensors whose locations are fixed. The employed framework demonstrates a very high correlation between the true and predicted values with an average coefficient value of 0.91 and 0.85 for both temperature and humidity, respectively. In other words, through a combination of the optimal number of fixed sensors and DNN architecture that performs multi-channel regression, we estimate the micro-climate of the greenhouse.