Towards a new generation of agricultural system data, models and knowledge products: Information and communication technology

Exploring the application of ICTs in decarbonizing the agriculture supply chain: A literature review and research agenda

The contemporary agricultural supply chain necessitates the integration of information and communication technologies to effectively mitigate the multifaceted challenges posed by climate change and rising global demand for food products. Furthermore, recent developments in information and communication technologies, such as blockchain, big data analytics, the internet of things, artificial intelligence, cloud computing, etc., have made this transformation possible. Each of these technologies plays a particular role in enabling the agriculture supply chain ecosystem to be intelligent enough to handle today's world's challenges. Thus, this paper reviews the crucial information and communication technologies-enabled agriculture supply chains to understand their potential uses and contemporary developments. The review is supported by 57 research papers from the Scopus database. Five research areas analyze the applications of the technology reviewed in the agriculture supply chain: food safety and traceability, security and information system management, wasting food, supervision and tracking, agricultural businesses and decision-making, and other applications not explicitly related to the agriculture supply chain. The study also emphasizes how information and communication technologies can help agriculture supply chains and promote agriculture supply chain decarbonization. An information and communication technologies application framework for a decarbonized agriculture supply chain is suggested based on the research's findings. The framework identifies the contribution of information and communication technologies to decision-making in agriculture supply chains. The review also offers guidelines to academics, policymakers, and practitioners on managing agriculture supply chains successfully for enhanced agricultural productivity and decarbonization.

Advancements in technology and innovation for sustainable agriculture: Understanding and mitigating greenhouse gas emissions from agricultural soils

In recent decades, Technology and Innovation (TI) have shown tremendous potential for improving agricultural productivity and environmental sustainability. However, the adoption and implementation of TI in the agricultural sector and its impact on the environment remain limited. To gain deeper insights into the significance of TI in enhancing agricultural productivity while maintaining environmental balance, this study investigates 21 agriculture-dependent Asian countries. Two machine learning techniques, LASSO (Least Absolute Shrinkage and Selection Operator) and Elastic-Net, are employed to analyze the data, which is categorized into three regional groups: ASEAN (Association of Southeast Asian Nations), SAARC (South Asian Association for Regional Cooperation), and GCC (Gulf Cooperation Council). The findings of this study highlight the heterogeneous nature of technology adoption and its environmental implications across the three country groups. ASEAN countries emerge as proactive adopters of relevant technologies, effectively enhancing agricultural production while simultaneously upholding environmental quality. Conversely, SAARC countries exhibit weaker technology adoption, leading to significant fluctuations in environmental quality, which in turn impact agricultural productivity. Notably, agricultural emissions of N2O (nitrous oxide) and CO2 (carbon dioxide) in SAARC countries show a positive association with agricultural production, while CH4 (methane) emissions have an adverse effect. In contrast, the study reveals a lack of evidence regarding technological adoption in agriculture among GCC countries. Surprisingly, higher agricultural productivity in these countries is correlated with increased N2O emissions. Moreover, the results indicate that deforestation and expansion of cropland contribute to increased agricultural production; however, this expansion is accompanied by higher emissions related to agricultural activities. This research represents a pioneering empirical analysis of the impact of TI and environmental emission gases on agricultural productivity in the three aforementioned country groups. It underscores the imperative of embracing relevant technologies to enhance agricultural output while concurrently ensuring environmental sustainability. The findings of this study provide valuable insights for policymakers and stakeholders in formulating strategies to promote sustainable agriculture and technological advancement in the context of diverse regional dynamics.

Digital twins and the future of precision mental health

Science faces challenges in developing much-needed precision mental health treatments to accurately identify and diagnose mental health problems and the optimal treatment for each individual. Digital twins (DTs) promise to revolutionize the field of mental health, as they are doing in other fields of science, including oncology and cardiology, where they have been successfully deployed. The use of DTs in mental health is yet to be explored. In this Perspective, we lay the conceptual foundations for mental health DTs (MHDT). An MHDT is a virtual representation of an individual’s mental states and processes. It is continually updated from data collected over the lifespan of the individual, and guides mental health professionals in diagnosing and treating patients based on mechanistic models and statistical and machine learning tools. The merits of MHDT are demonstrated through the example of the working alliance between the therapist and the patient, which is one of the most consistent mechanisms predicting treatment outcome.

Scientific modelling can be accessible, interoperable and user friendly: A case study for pasture and livestock modelling in Spain

This article describes the adaptation of a non-spatial model of pastureland dynamics, including vegetation life cycle, livestock management and nitrogen cycle, for use in a spatially explicit and modular modelling platform (k.LAB) dedicated to make data and models more interoperable. The aim is to showcase to the social-ecological modelling community the delivery of an existing, monolithic model, into a more modular, transparent and accessible approach to potential end users, regional managers, farmers and other stakeholders. This also allows better usability and adaptability of the model beyond its originally intended geographical scope (the Cantabrian Region in the North of Spain). The original code base (written in R in 1,491 lines of code divided into 13 files) combines several algorithms drawn from the literature in an opaque fashion due to lack of modularity, non-semantic variable naming and implicit assumptions. The spatiotemporal rewrite is structured around a set of 10 namespaces called PaL (Pasture and Livestock), which includes 198 interoperable and independent models. The end user chooses the spatial and temporal context of the analysis through an intuitive web-based user interface called k.Explorer. Each model can be called individually or in conjunction with the others, by querying any PaL-related concepts in a search bar. A scientific dataflow and a provenance diagram are produced in conjunction with the model results for full transparency. We argue that this work demonstrates key steps needed to create more Findable, Accessible, Interoperable and Reusable (FAIR) models beyond the selected example. This is particularly essential in environments as complex as agricultural systems, where multidisciplinary knowledge needs to be integrated across diverse spatial and temporal scales in order to understand complex and changing problems.

Barriers and opportunities of soil knowledge to address soil challenges: Stakeholders' perspectives across Europe

Climate-smart sustainable management of agricultural soil is critical to improve soil health, enhance food and water security, contribute to climate change mitigation and adaptation, biodiversity preservation, and improve human health and wellbeing. The European Joint Programme for Soil (EJP SOIL) started in 2020 with the aim to significantly improve soil management knowledge and create a sustainable and integrated European soil research system. EJP SOIL involves more than 350 scientists across 24 Countries and has been addressing multiple aspects associated with soil management across different European agroecosystems. This study summarizes the key findings of stakeholder consultations conducted at the national level across 20 countries with the aim to identify important barriers and challenges currently affecting soil knowledge but also assess opportunities to overcome these obstacles. Our findings demonstrate that there is significant room for improvement in terms of knowledge production, dissemination and adoption. Among the most important barriers identified by consulted stakeholders are technical, political, social and economic obstacles, which strongly limit the development and full exploitation of the outcomes of soil research. The main soil challenge across consulted member states remains to improve soil organic matter and peat soil conservation while soil water storage capacity is a key challenge in Southern Europe. Findings from this study clearly suggest that going forward climate-smart sustainable soil management will benefit from (1) increases in research funding, (2) the maintenance and valorisation of long-term (field) experiments, (3) the creation of knowledge sharing networks and interlinked national and European infrastructures, and (4) the development of regionally-tailored soil management strategies. All the above-mentioned interventions can contribute to the creation of healthy, resilient and sustainable soil ecosystems across Europe.

Development of Fırat Netlessphobia Scale and investigation of its psychometric properties

PURPOSE

The present study aimed to determine the level of Netlessphobia in the general population by developing the "Fırat Netlessphobia Scale."

DESIGN AND MEASURES

Trial form consisting of 19 items, which was tested in terms of language and psychometric validity, was applied to a sample group of 690 people. The reliability of the scale was verified by evaluating Cronbach's α reliability coefficient and test-retest consistency.

RESULTS

The developed "Fırat Netlessphobia Scale" can account for 60.7% of the variance related to Netlessphobia. Cronbach's α reliability coefficient of the scale was calculated as 0.93.

CONCLUSIONS

According to the findings, the Fırat Netlessphobia Scale is a cost-effective scale that facilitates the valid and reliable estimation of high variance and a low number of items.

PRACTICE IMPLICATIONS

This practical and cost-effective scale is widely applied owing to its small number of items and short expressions. The present study emphasizes the importance of exploring Netlessphobia and the need for future research in this area to recognize the risk groups and establish protection strategies.

ASAS-NANP symposium: mathematical modeling in animal nutrition: the progression of data analytics and artificial intelligence in support of sustainable development in animal science

A renewed interest in data analytics and decision support systems in developing automated computer systems is facilitating the emergence of hybrid intelligent systems by combining artificial intelligence (AI) algorithms with classical modeling paradigms such as mechanistic modeling (HIMM) and agent-based models (iABM). Data analytics have evolved remarkably, and the scientific community may not yet fully grasp the power and limitations of some tools. Existing statistical assumptions might need to be re-assessed to provide a more thorough competitive advantage in animal production systems towards sustainability. This paper discussed the evolution of data analytics from a competitive advantage perspective within academia and illustrated the combination of different advanced technological systems in developing HIMM. The progress of analytical tools was divided into three stages: collect and respond, predict and prescribe, and smart learning and policy making, depending on the level of their sophistication (simple to complicated analysis). The collect and respond stage is responsible for ensuring the data is correct and free of influential data points, and it represents the data and information phases for which data are cataloged and organized. The predict and prescribe stage results in gained knowledge from the data and comprises most predictive modeling paradigms, and optimization and risk assessment tools are used to prescribe future decision-making opportunities. The third stage aims to apply the information obtained in the previous stages to foment knowledge and use it for rational decisions. This stage represents the pinnacle of acquired knowledge that leads to wisdom, and AI technology is intrinsic. Although still incipient, HIMM and iABM form the forthcoming stage of competitive advantage. HIMM may not increase our ability to understand the underlying mechanisms controlling the outcomes of a system, but it may increase the predictive ability of existing models by helping the analyst explain more of the data variation. The scientific community still has some issues to be resolved, including the lack of transparency and reporting of AI that might limit code reproducibility. It might be prudent for the scientific community to avoid the shiny object syndrome (i.e., AI) and look beyond the current knowledge to understand the mechanisms that might improve productivity and efficiency to lead agriculture towards sustainable and responsible achievements.

Where Is My Crop? Data-Driven Initiatives to Support Integrated Multi-Stakeholder Agricultural Decisions

Globally, there has been an explosion of data generation in agriculture. With such a deluge of data available, it has become essential to create solutions that organize, analyze, and visualize it to gain actionable insights, which can guide farmers, scientists, or policy makers to take better decisions that lead to transformative actions for agriculture. There is a plethora of digital innovations in agriculture that implement big data techniques to harness solutions from large amounts of data, however, there is also a significant gap in access to these innovations among stakeholders of the value chains, with smallholder's farmers facing higher risks. Open data platforms have emerged as an important source of information for this group of producers but are still far from reaching their full potential. While the growing number of such initiatives has improved the availability and reach of data, it has also made the collection and processing of this information more difficult, widening the gap between those who can process and interpret this information and those who cannot. The Crop Observatories are presented in this article as an initiative that aims to harmonize large amounts of crop-specific data from various open access sources to build relevant indicators for decision making. Observatories are being developed for rice, cassava, beans, plantain and banana, and tropical forages, containing information on production, prices, policies, breeding, agronomy, and socioeconomic variables of interest. The Observatories are expected to become a lighthouse that attracts multi-stakeholders to avoid “not see the forest for the trees” and to advance research and strengthen crop economic systems. The process of developing the Observatories, as well as the methods for data collection, analysis, and display, is described. The main results obtained by the recently launched Rice Observatory (www.riceobservatory.org), and the about to be launched Cassava Observatory are presented, contextualizing their potential use and importance for multi-stakeholders of both crops. The article concludes with a list of lessons learned and next steps for the Observatories, which are also expected to guide the development of similar initiatives. Observatories, beyond presenting themselves as an alternative for improving data-driven decision making, can become platforms for collaboration on data issues and digital innovations within each sector.

Predicting Rice Production in Central Thailand Using the WOFOST Model with ENSO Impact

The World Food Studies Simulation Model (WOFOST) model is a daily crop growth and yield forecast model with interactions with the environment, including soil, agricultural management, and especially climate conditions. An El Niño–Southern Oscillation (ENSO) phenomenon directly affected climate change and indirectly affected the rice yield in Thailand. This study aims to simulate rice production in central Thailand using the WOFOST model and to find the relationship between rice yield and ENSO. The meteorological data and information on rice yields of Suphan Buri 1 variety from 2011 to 2018 in central Thailand were used to study the rice yields. The study of rice yield found that the WOFOST model was able to simulate rice yield with a Root Mean Square Error (RMSE) value of 752 kg ha−1, with approximately 16% discrepancy. The WOFOST model was able to simulate the growth of Suphan Buri 1 rice, with an average discrepancy of 16.205%, and Suphan Buri province had the least discrepancy at 6.99%. Most rice yield simulations in the central region were overestimated (except Suphan Buri) because the model did not cover crop damage factors such as rice disease or insect damage. The WOFOST model had good relative accuracy and could respond to estimates of rice yields. When an El Niño phenomenon occurs at Niño 3.4, it results in lower-than-normal yields of Suphan Buri 1 rice in the next 8 months. On the other hand, when a La Niña phenomenon occurs at Niño 3.4, Suphan Buri 1 rice yields are higher than normal in the next 8 months. An analysis of the rice yield data confirms the significant impact of ENSO on rice yields in Thailand. This study shows that climate change leads to impacts on rice production, especially during ENSO years.

Resources for image-based high-throughput phenotyping in crops and data sharing challenges

High-throughput phenotyping (HTP) platforms are capable of monitoring the phenotypic variation of plants through multiple types of sensors, such as red green and blue (RGB) cameras, hyperspectral sensors, and computed tomography, which can be associated with environmental and genotypic data. Because of the wide range of information provided, HTP datasets represent a valuable asset to characterize crop phenotypes. As HTP becomes widely employed with more tools and data being released, it is important that researchers are aware of these resources and how they can be applied to accelerate crop improvement. Researchers may exploit these datasets either for phenotype comparison or employ them as a benchmark to assess tool performance and to support the development of tools that are better at generalizing between different crops and environments. In this review, we describe the use of image-based HTP for yield prediction, root phenotyping, development of climate-resilient crops, detecting pathogen and pest infestation, and quantitative trait measurement. We emphasize the need for researchers to share phenotypic data, and offer a comprehensive list of available datasets to assist crop breeders and tool developers to leverage these resources in order to accelerate crop breeding.

Leveraging blockchain technology for circularity in agricultural supply chains: evidence from a fast-growing economy

Purpose

The authors originally explore the factors for blockchain technology (BCT) adoption in agricultural supply chains (ASCs) to enhance circularity and understand the dependencies, hierarchical structure and causalities between these factors.

Design/methodology/approach

Based on an extant literature review and expert opinion, the present study identified ten enablers for adopting BCT to leverage the circular economy (CE) practices in the ASCs. Then, using an integrated interpretive structural modeling and decision-making trial and evaluation laboratory (ISM-DEMATEL) approach, hierarchical and cause–effect relationships are established.

Findings

It was observed that traceability is the most prominent enabler from the CE perspective in ASCs. However, traceability, being a net effect enabler, will be realized through the achievement of other cause enablers, such as seamless connectivity and information flow and decentralized and distributed ledger technology. The authors also propose a 12 Rs framework for enhancing circularity in ASC operations.

Research limitations/implications

The paper identifies enablers to BCT adoption that will enhance circularity in ASC operations. The ISM hierarchical model is based on the driving and dependence powers of the enablers, and DEMATEL aids in identifying causal relationships among the enablers.

Practical implications

The study's findings and proposed 12 Rs framework may help the practitioners and policymakers devise effective BCT implementation strategies in ASCs, thereby empowering sustainability and circularity.

Originality/value

This study enriches the literature by identifying and modeling enablers for BCT adoption in ASCs. The study also proposes a new 12 Rs framework to help enhance ASC circularity.

Blockchain Applications in Agribusiness: A Systematic Review

Blockchain is a technology that can be applied in different sectors to solve various problems. As a complex system, agribusiness presents many possibilities to take advantage of blockchain technology. The main goal of this paper is to identify the purposes for which blockchain has been applied in the agribusiness sector, for which a PRISMA-based systematic review was carried out. The scientific literature corpus was accessed and selected from Elsevier’s Scopus and ISI of Knowledge’s Web of Science (WoS) platforms, using the PRISMA protocol procedures. Seventy-one articles were selected for analysis. Blockchain application in agribusiness is a novel topic, with the first publication dating from 2016. The technological development prevails more than blockchain applications since it has been addressed mainly in the Computer Sciences and Engineering. Blockchain applications for agribusiness management of financial, energy, logistical, environmental, agricultural, livestock, and industrial purposes have been reported in the literature. The findings suggest that blockchain brings many benefits when used in agribusiness supply chains. We concluded that the research on blockchain applications in agribusiness is only at an early stage, as many prototypes are being developed and tested in the laboratory. In the near future, blockchain will be increasingly applied across all economic sectors, including agribusiness, promoting greater reliability and agility in information with a reduced cost. Several gaps for future studies were observed, with significant value for science, industry, and society.

Grasping at digitalisation: turning imagination into fact in the sugarcane farming community

Nutrient runoff from catchments that drain into the Great Barrier Reef (GBR) is a significant source of stress for this World Heritage Area. An alliance of collaborative on-ground water quality monitoring (Project 25) and technologically driven digital application development (Digiscape GBR) projects were formulated to provide data that highlighted the contribution of a network of Australian sugar cane farmers, amongst other sources, to nutrient runoff. This environmental data and subsequent information were extended to the farming community through scientist-led feedback sessions and the development of specialised digital technology (1622™WQ) that help build an understanding of the nutrient movements, in this case nitrogen, such that farmers might think about and eventually act to alter their fertilizer application practices. This paper reflects on a socio-environmental sustainability challenge that emerged during this case study, by utilising the nascent concept of digi-grasping. We highlight the importance of the entire agricultural knowledge and advice network being part of an innovation journey to increase the utility of digital agricultural technologies developed to increase overall sustainability. We develop the digi-MAST analytical framework, which explores modes of being and doing in the digital world, ranging from 'the everyday mystery of the digital world (M)', through digital 'awareness (A)', digitally 'sparked' being/s (S), and finally the ability of individuals and/or groups to 'transform (T)' utilising digital technologies and human imaginations. Our digi-MAST framework allows us to compare agricultural actors, in this case, to understand present modes of digi-grasping to help determine the resources and actions likely to be required to achieve impact from the development of various forms of digital technological research outputs.

Shared Socio-economic Pathways for European agriculture and food systems: The Eur-Agri-SSPs

Scenarios describe plausible and internally consistent views of the future. They can be used by scientists, policymakers and entrepreneurs to explore the challenges of global environmental change given an appropriate level of spatial and sectoral detail and systematic development. We followed a nine-step protocol to extend and enrich a set of global scenarios - the Shared Socio-economic Pathways (SSPs) - providing regional and sectoral detail for European agriculture and food systems using a one-to-one nesting participatory approach. The resulting five Eur-Agri-SSPs are titled (1) Agriculture on sustainable paths, (2) Agriculture on established paths, (3) Agriculture on separated paths, (4) Agriculture on unequal paths, and (5) Agriculture on high-tech paths. They describe alternative plausible qualitative evolutions of multiple drivers of particular importance and high uncertainty for European agriculture and food systems. The added value of the protocol-based storyline development process lies in the conceptual and methodological transparency and rigor; the stakeholder driven selection of the storyline elements; and consistency checks within and between the storylines. Compared to the global SSPs, the five Eur-Agri-SSPs provide rich thematic and regional details and are thus a solid basis for integrated assessments of agriculture and food systems and their response to future socio-economic and environmental changes.

Sustainable Agriculture and Its Implementation Gap—Overcoming Obstacles to Implementation

There are numerous studies and publications about sustainable agriculture. Many papers argue that sustainable agriculture is necessary, and analyze how this goal could be achieved. At the same time, studies question the sustainability of agriculture. Several obstacles, including theoretical, methodological, personal, and practical issues, hinder or slow down implementation, resulting in the so-called implementation gap. This study addresses potential obstacles that limit the implementation of sustainable agriculture in practice. To overcome the obstacles and to improve implementation, different solutions and actions are required. This study aims to illustrate ways of minimizing or removing obstacles and how to overcome the implementation gap. Unfortunately, the diversity of obstacles and their complexity mean there are no quick and easy solutions. A broader approach that addresses different dimensions and stakeholders is required. Areas of action include institutionalization, assessment and system development, education and capacity building, and social and political support. To realize the suggestions and recommendations and to improve implementation, transdisciplinary work and cooperation between many actors are required.

Big Data Challenges and Opportunities in Agriculture

This article reviews various aspects of research concerning the background and state-of-the-art of big data in agriculture. This article focuses on data generation, storage, analysis and visualization in big data. In every phase, technical challenges and the latest advancement are discussed, and these discussions aim to provide a comprehensive overview and complete picture of this exciting area. This survey is concluded with a discussion on the application of big data in precision agriculture and its future directions.

Farmers' preferences for high-input agriculture supported by site-specific extension services: Evidence from a choice experiment in Nigeria

Agricultural extension to improve yields of staple food crops and close the yield gap in Sub-Saharan Africa often entails general recommendations on soil fertility management that are distributed to farmers in a large growing area. Site-specific extension recommendations that are better tailored to the needs of individual farmers and fields, and enabled by digital technologies, could potentially bring about yield and productivity improvements. In this paper, we analyze farmers' preferences for high-input maize production supported by site-specific nutrient management recommendations provided by an ICT-based extension tool that is being developed for extension services in the maize belt of Nigeria. We use a choice experiment to provide ex-ante insights on the adoption potentials of site-specific extension services from the perspective of farmers. We control for attribute non-attendance and account for class as well as scale heterogeneity in preferences using different models, and find robust results. We find that farmers have strong preferences to switch from general to ICT-enabled site-specific soil fertility management recommendations which lend credence to the inclusion of digital technologies in agricultural extension. We find heterogeneity in preferences that is correlated with farmers' resource endowments and access to services. A first group of farmers are strong potential adopters; they are better-off, less sensitive to risk, and are more willing to invest in a high-input maize production system. A second group of farmers are weak potential adopters; they have lower incomes and fewer productive assets, are more sensitive to yield variability, and prefer less capital and labor intensive production techniques. Our empirical findings imply that improving the design of extension tools to enable provision of information on the riskiness of expected outcomes and flexibility in switching between low-risk and high-risk recommendations will help farmers to make better informed decisions, and thereby improve the uptake of extension advice and the efficiency of extension programs.

Performance, farmer perception, and the routinisation (RO) moderation on ERP post-implementation

This study discusses the perceptions of the routinisation effects on the post-implementation and post-adoption of the enterprise resource planning (ERP) in farms. A theoretical model and nine hypotheses were proposed using factors according to the literature of resource-based view (RBV) approach and on the ERP impact on farm performance perceptions. This study contributes to the literature by testing empirically the moderation effect of routinisation on the RBV. A qualitative interview was applied to larger farmers where ERP was already in use and for the quantitative approach a sample of 448 answers was collected composed of 74% grain farmers, 14% cattle raising and milk producers, and 13% sugar cane and fruits farmers. The results reveal that the model explains 63% of the variation in the impact on farm performance. Our results show that routinisation moderates only the relationship between the impact on internal operations with impact on farm performance. The conclusions confirm the necessity to expand the RBV approach to the farmer perceptions, exploring other factors like the benefits and the impact of natural resources in the routinisation process. Finally, we propose a discussion of the development of Agriculture 4.0 in a resource-based view for the development of competitive advantage in the context of farms.

Modelling strategies for assessing and increasing the effectiveness of new phenotyping techniques in plant breeding

New types of phenotyping tools generate large amounts of data on many aspects of plant physiology and morphology with high spatial and temporal resolution. These new phenotyping data are potentially useful to improve understanding and prediction of complex traits, like yield, that are characterized by strong environmental context dependencies, i.e., genotype by environment interactions. For an evaluation of the utility of new phenotyping information, we will look at how this information can be incorporated in different classes of genotype-to-phenotype (G2P) models. G2P models predict phenotypic traits as functions of genotypic and environmental inputs. In the last decade, access to high-density single nucleotide polymorphism markers (SNPs) and sequence information has boosted the development of a class of G2P models called genomic prediction models that predict phenotypes from genome wide marker profiles. The challenge now is to build G2P models that incorporate simultaneously extensive genomic information alongside with new phenotypic information. Beyond the modification of existing G2P models, new G2P paradigms are required. We present candidate G2P models for the integration of genomic and new phenotyping information and illustrate their use in examples. Special attention will be given to the modelling of genotype by environment interactions. The G2P models provide a framework for model based phenotyping and the evaluation of the utility of phenotyping information in the context of breeding programs.

The assessment of supplementation requirements of grazing ruminants using nutrition models

This paper was aimed to summarize known concepts needed to comprehend the intricate interface between the ruminant animal and the pasture when predicting animal performance, acknowledge current efforts in the mathematical modeling domain of grazing ruminants, and highlight current thinking and technologies that can guide the development of advanced mathematical modeling tools for grazing ruminants. The scientific knowledge of factors that affect intake of ruminants is broad and rich, and decision-support tools (DST) for modeling energy expenditure and feed intake of grazing animals abound in the literature but the adequate predictability of forage intake is still lacking, remaining a major challenge that has been deceiving at times. Despite the mathematical advancements in translating experimental research of grazing ruminants into DST, numerous shortages have been identified in current models designed to predict intake of forages by grazing ruminants. Many of which are mechanistic models that rely heavily on preceding mathematical constructions that were developed to predict energy and nutrient requirements and feed intake of confined animals. The data collection of grazing (forage selection, grazing behavior, pasture growth/regrowth, pasture quality) and animal (nutrient digestion and absorption, volatile fatty acids production and profile, energy requirement) components remains a critical bottleneck for adequate modeling of forage intake by ruminants. An unresolved question that has impeded DST is how to assess the quantity and quality, ideally simultaneously, of pasture forages given that ruminant animals can be selective. The inadequate assessment of quantity and quality has been a hindrance in assessing energy expenditure of grazing animals for physical activities such as walking, grazing, and forage selection of grazing animals. The advancement of sensors might provide some insights that will likely enhance our understanding and assist in determining key variables that control forage intake and animal activity. Sensors might provide additional insights to improve the quantification of individual animal variation as the sensor data are collected on each subject over time. As a group of scientists, however, despite many obstacles in animal and forage science research, we have thrived, and progress has been made. The scientific community may need to change the angle of which the problem has been attacked, and focus more on holistic approaches.

A Novel ArcGIS Toolbox for Estimating Crop Water Demands by Integrating the Dual Crop Coefficient Approach with Multi-Satellite Imagery

Advances in information and communication technologies facilitate the application of complex models for optimizing agricultural water management. This paper presents an easy-to-use tool for determining crop water demands using the dual crop coefficient approach and remote sensing imagery. The model was developed using Python as a programming language and integrated into an ArcGIS (geographic information system) toolbox. Inputs consist of images from satellites Landsat 7 and 8, and Sentinel 2A, along with data for defining crop, weather, soil type, and irrigation system. The tool produces a spatial distribution map of the crop evapotranspiration estimates, assuming no water stress, which allows quantifying the water demand and its variability within an agricultural field with a spatial resolution of either 10 m (for Sentinel) or 30 m (for Landsat). The model was validated by comparing the estimated basal crop coefficients (Kcb) of lettuce and peach during an irrigation season with those tabulated as a reference for these crops. Good agreements between Kcb derived from both methods were obtained with a root mean squared error ranging from 0.01 to 0.02 for both crops, although certain underestimations were observed resulting from the uneven crop development in the field (percent bias of −4.74% and −1.80% for lettuce and peach, respectively). The developed tool can be incorporated into commercial decision support systems for irrigation scheduling and other applications that account for the water balance in agro-ecosystems. This tool is freely available upon request to the corresponding author.

Role of Modelling in International Crop Research: Overview and Some Case Studies

Crop modelling has the potential to contribute to global food and nutrition security. This paper briefly examines the history of crop modelling by international crop research centres of the CGIAR (formerly Consultative Group on International Agricultural Research but now known simply as CGIAR), whose primary focus is on less developed countries. Basic principles of crop modelling building up to a Genotype × Environment × Management × Socioeconomic (G × E × M × S) paradigm, are explained. Modelling has contributed to better understanding of crop performance and yield gaps, better prediction of pest and insect outbreaks, and improving the efficiency of crop management including irrigation systems and optimization of planting dates. New developments include, for example, use of remote sensed data and mobile phone technology linked to crop management decision support models, data sharing in the new era of big data, and the use of genomic selection and crop simulation models linked to environmental data to help make crop breeding decisions. Socio-economic applications include foresight analysis of agricultural systems under global change scenarios, and the consequences of potential food system shocks are also described. These approaches are discussed in this paper which also calls for closer collaboration among disciplines in order to better serve the crop research and development communities by providing model based recommendations ranging from policy development at the level of governmental agencies to direct crop management support for resource poor farmers.

Review: Precision nutrition of ruminants: approaches, challenges and potential gains

A plethora of sensors and information technologies with applications to the precision nutrition of herbivores have been developed and continue to be developed. The nutritional processes start outside of the animal body with the available feed (quantity and quality) and continue inside it once the feed is consumed, degraded in the gastrointestinal tract and metabolised by organs and tissues. Finally, some nutrients are wasted via urination, defecation and gaseous emissions through breathing and belching whereas remaining nutrients ensure maintenance and production. Nowadays, several processes can be monitored in real-time using new technologies, but although these provide valuable data 'as is', further gains could be obtained using this information as inputs to nutrition simulation models to predict unmeasurable variables in real-time and to forecast outcomes of interest. Data provided by sensors can create synergies with simulation models and this approach has the potential to expand current applications. In addition, data provided by sensors could be used with advanced analytical techniques such as data fusion, optimisation techniques and machine learning to improve their value for applications in precision animal nutrition. The present paper reviews technologies that can monitor different nutritional processes relevant to animal production, profitability, environmental management and welfare. We discussed the model-data fusion approach in which data provided by sensor technologies can be used as input of nutrition simulation models in near-real time to produce more accurate, certain and timely predictions. We also discuss some examples that have taken this model-data fusion approach to complement the capabilities of both models and sensor data, and provided examples such as predicting feed intake and methane emissions. Challenges with automatising the nutritional management of individual animals include monitoring and predicting of the flow of nutrients including nutrient intake, quantity and composition of body growth and milk production, gestation, maintenance and physical activities at the individual animal level. We concluded that the livestock industries are already seeing benefits from the development of sensor and information technologies, and this benefit is expected to grow exponentially soon with the integration of nutrition simulation models and techniques for big data analysis. However, this approach may need re-evaluating or performing new empirical research in both fields of animal nutrition and simulation modelling to accommodate a new type of data provided by the sensor technologies.

Orchard Free Space and Center Line Estimation Using Naive Bayesian Classifier for Unmanned Ground Self-Driving Vehicle

In the case of autonomous orchard navigation, researchers have developed algorithms that utilize features, such as trunks, canopies, and sky in orchards, but there are still various difficulties in recognizing free space for autonomous navigation in a changing agricultural environment. In this study, we applied the Naive Bayesian classification to detect the boundary between the trunk and the ground and propose an algorithm to determine the center line of free space. The naïve Bayesian classification requires a small number of samples for training and a simple training process. In addition, it was able to effectively classify tree trunk’s points and noise points of the orchard, which are problematic in vision-based processing, and noise caused by small branches, soil, weeds, and tree shadows on the ground. The performance of the proposed algorithm was investigated using 229 sample images obtained from an image acquisition system with a Complementary Metal Oxide Semiconductor (CMOS) Image Sensor (CIS) camera. The center line detected by the unaided-eye manual decision and the results extracted by the proposed algorithm were compared and analyzed for several parameters. In all compared parameters, extracted center line was more stable than the manual center line results.

Next generation data systems and knowledge products to support agricultural producers and science-based policy decision making

Research on next generation agricultural systems models shows that the most important current limitation is data, both for on-farm decision support and for research investment and policy decision making. One of the greatest data challenges is to obtain reliable data on farm management decision making, both for current conditions and under scenarios of changed bio-physical and socio-economic conditions. This paper presents a framework for the use of farm-level and landscape-scale models and data to provide analysis that could be used in NextGen knowledge products, such as mobile applications or personal computer data analysis and visualization software. We describe two analytical tools - AgBiz Logic and TOA-MD - that demonstrate the current capability of farmlevel and landscape-scale models. The use of these tools is explored with a case study of an oilseed crop, Camelina sativa, which could be used to produce jet aviation fuel. We conclude with a discussion of innovations needed to facilitate the use of farm and policy-level models to generate data and analysis for improved knowledge products.

Brief history of agricultural systems modeling

Agricultural systems science generates knowledge that allows researchers to consider complex problems or take informed agricultural decisions. The rich history of this science exemplifies the diversity of systems and scales over which they operate and have been studied. Modeling, an essential tool in agricultural systems science, has been accomplished by scientists from a wide range of disciplines, who have contributed concepts and tools over more than six decades. As agricultural scientists now consider the "next generation" models, data, and knowledge products needed to meet the increasingly complex systems problems faced by society, it is important to take stock of this history and its lessons to ensure that we avoid re-invention and strive to consider all dimensions of associated challenges. To this end, we summarize here the history of agricultural systems modeling and identify lessons learned that can help guide the design and development of next generation of agricultural system tools and methods. A number of past events combined with overall technological progress in other fields have strongly contributed to the evolution of agricultural system modeling, including development of process-based bio-physical models of crops and livestock, statistical models based on historical observations, and economic optimization and simulation models at household and regional to global scales. Characteristics of agricultural systems models have varied widely depending on the systems involved, their scales, and the wide range of purposes that motivated their development and use by researchers in different disciplines. Recent trends in broader collaboration across institutions, across disciplines, and between the public and private sectors suggest that the stage is set for the major advances in agricultural systems science that are needed for the next generation of models, databases, knowledge products and decision support systems. The lessons from history should be considered to help avoid roadblocks and pitfalls as the community develops this next generation of agricultural systems models.

Next generation agricultural system models and knowledge products: Synthesis and strategy

The purpose of this Special Issue of Agricultural Systems is to lay the foundation for the next generation of agricultural systems data, models and knowledge products. In the Introduction to this Special Issue, we described a vision for accelerating the rate of agricultural innovation and meeting the growing global need for food and fiber. In this concluding article of the NextGen Special Issue we synthesize insights and formulate a strategy to advance data, models, and knowledge products that are consistent with this vision. This strategy is designed to facilitate a transition from the current, primarily supply-driven approach toward a more demand-driven approach that would address key Use Cases where better data, models and knowledge products are seen by end-users as essential to meet their needs.