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With the increasing food demand, it is essential to plan for a future where climate change affects the food sector. The policy making necessary to ensure the food demand must be driven by a thorough assessment of the impact climate change has on the food sector. Such an assessment can be facilitated by building impact models focusing on different aspects of the food industry. In this paper, we build an impact model to demonstrate the effect the climate has on the dairy (raw milk) industry. This impact model is built using raw milk data collected from more than 200 dairy farms in two climate zones over a 6-year timespan. The analysis using dairy variables like milk volume, fat, lactose, protein, somatic cell counts, and total bacterial counts (TBC) revealed correlations with climate variables such as temperature, humidity, wind, radiation, and precipitation. To enable prediction of these dairy variables over different climate scenarios, regression and forecasting models are built individually. Rather than temperature, the most significant predictors are the radiation, humidity, wind, and precipitation. These models are tested for their generalizability over multiple climate zones. With prediction errors always below 20%, these models can be exploited to quantitatively assess the effect of climate change on milk production. Overall, this research contributes to the design of strategies towards a resilient, climate change-proof food system. [Display omitted] • Milk volume, fat, protein, SCC and TBC from hundreds of farms are analyzed. • Correlations with temperature, humidity, wind and rain are identified. • Principal component analysis reveals correlations between milk variables. • Milk traits predictive models are built and tested for generalization. • The most impactful predictors are radiation, humidity, wind and rain. [ABSTRACT FROM AUTHOR]

• We proposed the conception of horticultural phenomics. • High-throughput aerospace-sky-ground phenotypic facilities in horticultural phenomics have also had the embryonic form and have been introduced, analyzed, distinguish systematically in this paper. • Horticultural phenomics towards multiscale, multidimension, multiperiod at present and is shown in this paper. • Application of plant phenomics in horticulture research have been summarized, such as omics research and horticultural breeding. With the development of advanced sensors, artificial intelligence, and computer vision, horticultural phenomics has made great progress in recent years. However, there is a lack of systematic reviews to discuss the horticultural phenomics and its applications at present. In this paper, we aim to fill this gap by providing a comprehensive review of the past developments, recent advances and existing challenges. Furthermore, we propose conception and give our prospects of high-throughput horticultural phenomics. We envision that horticultural phenomics will evolve towards multiscale, multidimension, multiperiod approach in the near future. This transformation will enable researchers to better realize the physiological processes, understand the genetic composition and develop new cultivars of horticultural plants that are better adapted to specific environments. Consequently, it will greatly promote the omics research in horticultural plants. In addition to advancing scientific research, high-throughput horticultural phenomics can also assist growers in efficient crop management, leading to improved yield and quality of horticultural products. However, it is important to acknowledge the challenges that need to be overcome. Once these challenges are addressed, high-throughput horticultural phenomics will become increasingly important in the fight against climate changes and maximizing profits. [ABSTRACT FROM AUTHOR]

• This paper uses a scientific approach to expand the olive crop cultivation by modeling environmental factors that impact olive growth. • The study focuses on defining the lands actually planted with the olive crop and then comparing them with the new lands that were discovered and identified by the model that created using ArcGIS software. • The model included all environmental factors like soil, temperature, rainfa ll, elevation and The Normalized Difference Vegetation Index (NDVI) • The images of the vegetation were downloaded from eMODIS NDVI and eVIRS NDVI satellites. Since ancient times, civilizations have expanded their agricultural activities, particularly in crops related to human nutrition, where the process of agricultural expansion followed a traditional strategy. These random methods are based on searching for lands where water is available, whether through rain or irrigation, so these methods are not feasible. This paper uses a scientific approach to expand the olive crop cultivation by modeling environmental factors that impact olive growth by searching for other lands that have the same climatic characteristics as cultivated lands. The study focuses on defining the lands actually planted with the olive crop and then comparing them with the new lands that were discovered and identified by the model that created using ArcGIS software. It is noted that these new lands are completely similar to the actual lands in terms of soil and climate. Therefore, we selected five time periods in November, December, January and February from 2016 to 2020. The images of the vegetation were downloaded from eMODIS NDVI and eVIRS NDVI satellites. The model included most of the main environmental factors like temperature, rainfall, elevation, Normalized Difference Vegetation Index (NDVI) In addition to the most important factor, which is the soil. By comparing the suggested areas to grow olive crop with actual areas, the model illustrates and explains the suitability of the suggested area for olive cultivation. By examining the final map and graphs from the model, the results indicate a very high success rate in the productivity of olives in the suggested areas. By the other hand this study reinforces the concept of Land-use/land-cover (LULC), which is one of the crucial concepts that are concerned with the change of land from one state to another over time series according to climatic changes. [ABSTRACT FROM AUTHOR]

Highlights • We assessed land suitability for rainfed maize under current and future climates. • A plausible range of future climate was derived from multi-model projections. • Interaction among criteria was handled with a fuzzy measure modelling approach. • About 91% of W/Africa is at least moderately suitable for maize in current climate. • Future climate change will affect suitability for maize in >43% of West Africa. Abstract Crop production in West Africa is largely under rainfed conditions, making the system vulnerable to the impacts of climate change. However, the impact of future climate on the geographic range of many crops in West Africa is still uncertain. This is exacerbated by considerable uncertainty in projecting future West African climate by global circulation models (GCMs). In this paper, the current land suitability for rainfed maize (Zea mays L.) production in West Africa and how it would change in response to a range of future climate changes were assessed. A novel non-additive approach involving the integration of a fuzzy measure modelling procedure and the Choquet integral to deal with non-linear relationships among criteria was introduced. The supervised and unsupervised variants of the approach and the Ordered Weighted Averaging (OWA) aggregators were evaluated and compared using data on the current climate, soil characteristics, and actual maize yield estimates at some locations across the region. The supervised fuzzy measure modelling outperformed the unsupervised variant and the OWA operators, demonstrating the importance of considering interaction among criteria and incorporating human reasoning in multicriteria analysis. Consequently, the supervised modelling approach was used to estimate suitability scores for rainfed maize for the current and a range of plausible future climates which were generated from unique combinations of temperature and rainfall percentile values (5th, 50th and 95th) from 25 and 32 GCMs projections under the RCPs 2.6 and 8.5 scenarios, respectively. Under the current climate in West Africa, the combined suitable/highly suitable areas for rainfed maize production are in the humid savanna zones. In contrast, the forest region to the south exhibited moderate suitability with the semi-arid savanna zones being marginally suitable. Relative to the current climate in which 91.3% of West Africa is at least moderately suitable for maize production, suitability for maize will change in at least 43% of West Africa in response to future climate change. Across all scenarios and years, future suitability in the semi-arid region of the Sudano-Sahelian savannas shows strong sensitivity to uncertainty in rainfall projections. However, regardless of the trajectory of future rainfall projection, the 95th percentile temperature projection will decrease suitability in about 15% of the West African region by 2080 under the RCP8.5 scenario. Because of the incorporation of uncertainties of future climate projections in this analysis, the results from this study can be used in the development of flexible climate adaptation strategies. [ABSTRACT FROM AUTHOR]

Highlights • Proposed framework identifies the similarity among the weather condition over years. • A powerful domain specific weather-based decision support system (DSS) can be built using this framework. • Significant reuse of the developed content in a weather-based DSS can be achieved using this framework. Abstract Weather-based decision support systems (DSSs) are being built to improve the efficiency of the production systems in the domains of healthcare, agriculture, transport, governance and so on. Normally, a weather condition (WC) is represented by the statistical values of weather variables for a given duration (e.g. a day or a week). In a weather-based DSS, given a WC, the domain experts prepare the appropriate suggestions to improve the efficiency of the stakeholders. Normally, once the domain experts prepare a suggestion for a given WC that belongs to certain period (e.g. year or season), there is a scope to reuse the same suggestion for the similar WCs of other period(s). As a result, the performance of the DSS could be improved. In this paper, to improve reuse , we have proposed a notion of category-based WC (CWC) which is formed by using the categories of weather variables in the respective domain. By considering the context of agromet advisory service operated by India Meteorological Department (IMD) and the corresponding weather categories provided by IMD, we have analyzed the extent of reuse among CWCs by conducting the experiments on 30 years of weather data collected at Rajendranagar, Hyderabad, Telangana state. The experiments are conducted by considering two types of CWCs with the duration equal to one day and five days. By varying the number of weather variables in CWC from one to five, we have computed the extent of reuse among CWCs of different periods of the following period types: year, season, and phenophases (i.e., growth stages) of the Rice crop. The results show that there is a significant similarity among the CWCs of the given period and the CWCs of the preceding periods of each period type. For any domain including agriculture, the results provide an opportunity to improve the efficiency of weather-based agricultural DSSs by improving the reuse of the weather-based suggestions. [ABSTRACT FROM AUTHOR]

While the application of nitrogen (N) fertilizer increases crop productivity, the amount of N not taken up by the crop is released as reactive N causing adverse environmental effects, which are associated with short-term climate variations and consist of nitrous oxide emissions, ammonia volatilization, and nitrate leaching. Finding the optimum rate of N application accounting for both economic and environmental aspects is challenging because it varies according to soil texture and climatic conditions. In this paper, a model-based methodology is developed to identify the ecophysiological optimum N rates at which the applied N leads to minimum N excess with little loss in maximum achievable yield. A slope-based method is proposed to identify the optimum nitrogen use efficiency (NUE opt ) and to determine the corresponding optimum N rate (Nopt) for each growing season in given agroclimatic regions according to soil properties. It uses the yield predicted by a process-based crop model, long time series of climate data, and a newly proposed Mitscherlich Baule-plateau (MB-P) function. The NUE opt is identified by evaluating the linearity of the relationship between yield and N opt , and the reduction in yield compared to maximum achievable yield for a given soil and various growing seasons. We illustrate the methodology and its performance by presenting corn yield response to N rate applications predicted by the STICS crop model for dominant soils with contrasting properties with 48–61 years of daily climate data from five major regions located along an agroclimatic gradient of the Mixedwood Plains ecozone in Canada (42.3°N 83°W - 46.8°N 71°W), which is the main grain corn production ecozone of Canada. Our case study in this ecozone indicates that a newly proposed yield MB-P function outperforms the two commonly used functions, i.e., linear-plateau, and MB functions. The proposed methodology provides valuable information such as the likelihood of achieving a yield in a given region, the recommended N rate for given expected yield, the consequent N excess or deficit, and the reduction in yield compared to maximum achievable yield. The proposed methodology can be used in any region where a crop model has been adapted, and soil properties and time series of climate data are available. This approach sets the stage for an in-depth environmental assessment on reactive N within an integrated decision support system. [ABSTRACT FROM AUTHOR]

Modelling and simulation are of high importance in agriculture and climate change research. This paper aims to simulate and analyse the sour cherry product lines in Hungary. The model presented below is suitable to analyse the market share, the cost and income relations as well as the relation structure of the sour cherry product lines. The vertical levels of the model (soil and climatic conditions, varieties, size of orchards, product type and trade channels) have individual values added from the aspect of the end product. Theoretically, all elements can be connected to any element of the next level, but there can be “prohibited contacts” because of professional, regulation or production practice reasons. The model makes possible to evaluate, compare and filter the different product lines, analyse the effects of the climate change or the use of different varieties, and thereby provides support for strategic planning and decision making. [ABSTRACT FROM AUTHOR]

• This paper presents a review on monitoring and advanced control strategies for precision irrigation. • Summary and discussions on most of the related work and future research trends in this area were carried out. • This review paper will be helpful in identifying research gaps and directions for readers and researchers. The demand for freshwater is on the increase due to the rapid growth in the world's population while the effect of global warming and climate change cause severe threat to water use and food security. Consequently, irrigation systems are tremendously utilized by many farmers all over the world with its associated high amount of water consumption from various sources posing a major concern. This necessitates the increased focus on improving the efficiency of water usage in irrigation agriculture. The advent and rapid successes of the Internet of Things (IoT) and advanced control strategies are being leveraged to achieve improved monitoring and control of irrigation farming. In this review, a thorough search for literature on irrigation monitoring and advanced control systems highlighting the research works within the past ten years are presented. Attention is paid on recent research works related to the monitoring and advance control concepts for precision irrigation. It is expected that this review paper will serve as a useful reference to enhance reader's knowledge on monitoring and advanced control opportunities related to irrigation agriculture as well as assist researchers in identifying directions and gaps to future research works in this field. [ABSTRACT FROM AUTHOR]

Smart agriculture aims to improve the quality and quantity of crops by efficiently managing available resources. One of the main components of smart agriculture is precision irrigation which applies the required amount of water at the right time to crops. Crop evapotranspiration (E T c ) prediction can contribute to managing the irrigation strategies effectively. Although several methods have been introduced for estimating E T c values, these methods are still associated with various challenges and limitations (high cost, time-consuming, and meteorological data unavailability). The current study is motivated by the desire to create deep learning (DL) based models capable of estimating E T c reliably to eliminate the above-mentioned limitations and forecast future E T c values for adaptation strategies. In this paper, two-hybrid DL models, i.e., Convolution Neural Network-eXtreme Gradient Boosting (CNN-XGB) and Convolution Neural Network-Support Vector Regression (CNN-SVR) are proposed to estimate daily E T c values of wheat and rice crops. Further, limited climate data (minimum temperature (T m i n), maximum temperature (T m a x), mean temperature (T m e a n) and solar radiation (R s)) is used for the prediction of E T c values to handle data-scarce situations. Also, the future climate data obtained using two emission scenarios: Representative Concentration Pathways (RCP) 4.5 and RCP 8.5 for the time period 2023–2033, are used to project changes in E T c . The results demonstrate that the proposed hybrid models provide satisfactory performance with the Nash–SutcliffeEfficiency (NSE) = 0.95 and 0.976 for rice and wheat E T c values, respectively. The simulation of future data reveals the increase in T m i n by 7.03%, 7.33%, and T m a x by 10.5%, 11.5% for RCP 4.5 and RCP 8.5 respectively. Also, an increase in E T c of rice crop has been reported by 20%–22% while increment of wheat E T c has been noticed by 3%–4%. Thus, the proposed approach efficiently estimates E T c of wheat and rice crops using limited climate data and could assist water resource managers in achieving agricultural water sustainability. • CNN-XGB and CNN-SVR are proposed to predict wheat and rice E T c . • The proposed models provide reliable accuracy with limited input climate data. • The future projection of wheat and rice E T c indicates an increase in water needs. • The proposed models could assist agriculturists in making adaptation strategies. [ABSTRACT FROM AUTHOR]

Climate change, the water crisis, and population growth add new challenges for food production. The modernization of agricultural methods is essential to increase production rates and preserve natural resources. Smart agriculture provides resources that can enhance farming tasks by efficiently controlling actuators, optimizing utility and resource use, managing production, maximizing profit, and minimizing costs. For these technologies to become popular, they must have a high level of reliability and safety. To improve the reliability in Smart Agriculture, this paper proposes CEIFA, a low-cost, hybrid anomaly detector capable of identifying failures, faults, errors and attacks that impact these systems. CEIFA operates on local or remote cloud servers, filtering data sent by agricultural system sensors. It can operate on resource-restricted devices and save financial resources related to computing costs. Real tests on a set of faults, failures, and errors point to an efficiency greater than 95% in anomaly detection. • A low-cost hybrid anomaly detector that operates at the edge and in the cloud. • Analyzes data for failures, faults and errors in perception layer devices. • Analyze data for traces of cyberattacks. • Anomaly detection efficiency is over 90%. • Low computational cost allows running on constrained devices. [ABSTRACT FROM AUTHOR]

This paper uses climatic measures from a greenhouse to get a fuzzy model of the internal temperature, with the fuzzy model obtained; two control actions are used to control the internal temperature. The fuzzy control is developed by using the inverted fuzzy model. Due the number of climatic variables and to the condition of having a monotonic fuzzy model, there were used two membership functions for the fuzzy partition of each variable. It was used a fuzzy singleton model which is tuned by using batch least squares and updated with recursive least squares. The control actuators are: a proportional servo-valve for the heating, it works at nights when frozen can occur, and the ventilation, it is used during the day to avoid high temperatures, these signals are excluding mutually because the humidity or hot air can be lost generating an extra cost. The greenhouse is used for tomatoes cultivation. [ABSTRACT FROM AUTHOR]

In this paper, an automatic Smart Irrigation Decision Support System, SIDSS, is proposed to manage irrigation in agriculture. Our system estimates the weekly irrigations needs of a plantation, on the basis of both soil measurements and climatic variables gathered by several autonomous nodes deployed in field. This enables a closed loop control scheme to adapt the decision support system to local perturbations and estimation errors. Two machine learning techniques, PLSR and ANFIS, are proposed as reasoning engine of our SIDSS. Our approach is validated on three commercial plantations of citrus trees located in the South-East of Spain. Performance is tested against decisions taken by a human expert. [ABSTRACT FROM AUTHOR]

Australia’s agriculture industry, particularly in the north, is characterised by supply chains of long travel distances, often in excess of 2500 km and costing up to 35% of farm gate price. Such travel distances increase the vulnerability of the industry to climatic variability and extreme events. Infrastructure investments in roads, bridges, processors and storage, along with changes in policy, have the potential to substantially reduce costs and increase resilience of the agriculture industries. In this paper, we outline the model, TRAnsport Network Strategic Investment Tool (TRANSIT) which is based on ArcGIS, and utilizes the Origin to Destination Cost Matrix solver within the Network Analyst toolkit. TRANSIT estimates the transport costs for all movements between enterprises, accommodating road conditions, vehicle types, vehicle access restrictions and regulatory requirements. TRANSIT was applied to the northern Australia livestock industry, consisting of 12 million cattle across 10,000 enterprises and 89,000 unique trips between these enterprises. Its ability to estimate the transport benefits from road upgrades, new processing facilities and biosecurity changes are shown using three priority case studies identified by industry and government. [ABSTRACT FROM AUTHOR]

A significant problem exists concerning contamination of the environment, especially air pollution, and the consequent climatic change. Considering that agricultural vehicles that use fossil fuels emit significant amounts of atmospheric pollutants, the main objective of this paper is to include techniques to reduce the fuel consumption in the controls system of robotic tractors used in agriculture tasks and thereby reduce the atmospheric emissions from these automated applications. To achieve this goal, the first step is to analyze fuel consumption in real time for each of the applications to be improved and to implement the consumption model of a robotic tractor for each task, considering the mechanical energy variations, the performance losses, the energy used to overcome friction and the energy required by the given task. For calculating the mechanical energy, the model considers the potential energy of the system, which is a function of the mass, elevation and gravity. The terrain elevations are estimated from GeoTIFF images of DEM data, which have a pixel size equal to 1 arc second (approximately 30 m at the Equator), and an accuracy of integer meters. Regarding the system mass, the possible loss of mass from applying the treatment is considered. For estimating the frictional forces, the rolling resistance coefficient of the terrain surface conditions is used. The consumption model has been validated experimentally using real agricultural vehicles and implements within the RHEA project (FP7-NMP 245986), in which the instantaneous fuel consumption was measured. This fuel reduction method is applied to three different treatments: weed control on herbaceous crops through the spraying of herbicides, weed control on fire-resistant crops with wide furrows through plowing and flame treatment, and pest control on trees through fumigation using insecticides. Finally, a fuel reduction procedure is applied to each task using the system model implemented to predict the energy requirements. This enables one to find the optimum path plan with respect to fuel consumption. These theoretical results are compared with the experimental results. In addition, the goal is to demonstrate the fuel reduction technique by performing field experiments to show that the use of this method of fuel reduction leads to an reduced fuel consumption and thus reduces atmospheric emissions from agricultural tasks. The results obtained revealed that this fuel reduction method significantly reduces the energy requirements, with the consequent reduction in fuel consumption and atmospheric pollutant emissions. [ABSTRACT FROM AUTHOR]

• Review of studies in cattle identification, body condition score evaluation, and weight estimation. • Comparison of intelligent perception techniques in cattle monitoring. • Discussion on existing challenges and future research opportunities for PLF. There has been an increasing demand for animal protein due to several factors such as global population growth, rising incomes, etc. However, farming productivity is stagnating due to a mix of traditional practice, climate change, socio-economic, and environmental phenomena. Precision livestock farming, with intelligent perception tools at its core, and vast amounts of data being acquired from different sensors or platforms, has the ability to analyse individual animal for improved management, and the potential to dramatically enhance farm productivity. In order to facilitate research and promote the development of related areas, this review summarises and analyses the main existing techniques used in precision cattle farming, focusing on those related to identification, body condition score evaluation, and live weight estimation. More than 100 relevant papers have been discussed in a cohesive manner. From this review and extensive discussions of recent trends, we anticipate that intelligent perception for precision cattle farming will develop through non-contact, high precision, automated technologies, combined with emerging 3D model reconstruction and deep learning technologies. Existing challenges and future research opportunities will also be highlighted and discussed. [ABSTRACT FROM AUTHOR]