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[Display omitted] • Self-folded honeycomb structure (SHS) was fabricated via stimuli-responsive materials. • Origami-inspired design for SHSs with energy-absorption properties was established. • Relationship of design, structure, and compression performance was elucidated. • Energy-absorption of 5-layered SHS was 6.67 times higher than that of 1-layered SHS. • Damage factor for protected object was successfully removed via pre-straining. The origami technique has been employed in the medical, space, and agricultural industries owing to its low cost and ease of use. In particular, the use of stimuli-responsive materials to automate origami fabrication has garnered interest. In this study, self-folded honeycomb structures (SHSs) were fabricated using the self-folding technique. A sheet of paper undergoes self-folding without human intervention via the physicochemical reactions between the paper and printing solution. Six SHSs with different mechanical properties were fabricated. The relationship between the design and structural parameters was examined. The energy-absorption performance of the structure improved by a factor of 15.2 on employing the optimal parameters. A consistent relationship between the design and compression performance was also discussed. By applying stacking technology, the energy-absorption performance increased by an average of 1753 N·mm per layer. The application of a pre-strain eliminated the compressive force peak that could damage the protected object, while maintaining 90 % of the energy-absorption performance. The proposed technology shows potential for the fabrication of tailored energy absorbers using stimuli-responsive materials, providing a groundbreaking technology for the packaging industry. [ABSTRACT FROM AUTHOR]

• Updated review paper on blockchain-based smart contract adoption in agriculture and food industry globally by the Governments and the private entities. • Comprehensive listing of the most popular smart contract platforms among the developer community and their notable features. • Overview of current obstacles and emerging trends of blockchain technology in the agriculture and food sector. Blockchain Technology has been finding industry-wide applications over the decade ranging from supply chain traceability to tamper-proof land registrations; facilitated by smart contracts and non-fungible tokens. The inherent immutability, transparency, and security of blockchain networks can transform the agriculture and food industry. Although several reviews have already been written, a complete in-depth analysis covering the popular smart contract platforms, current research progress concerning the agriculture and food industry, and applications of blockchain technology other than supply chain traceability has not been well documented. This paper discusses smart contracts-the computer programs that run on the blockchain, the smart contract life cycle, some of the most popular smart contract platforms among the developer community, and smart contact applications in the agriculture and food industry which includes a gist of recent developments in the Government and private sector across continents and the challenges and future trends associated with the use of this rapidly evolving technology. [ABSTRACT FROM AUTHOR]

• A simple µPAD based on Griess chemistry is presented for rapid determination of iodate. • A wide linear range offers potential applications of this µPAD for determining iodate levels in different kinds of samples. • The device is easy to fabricate with rapid operation. We report a convenient microfluidic paper-based analytical device (µPAD) for the determination of iodate. The µPAD was fabricated using an ink-stamp with a simple circle design. Colorimetric detection of iodate employs the oxidation of hydroxylamine by the iodate to generate nitrite which reacts with the Griess reagent producing an intense magenta color. The images were recorded by a digital camera, and converted to a green scale color intensity by ImageJ. Calibration of iodate was linear in the range of 50–400 mg L−1 with a detection limit of 38.1 mg L−1. With such a wide linear range, the method can be applied to various kinds of samples with the entire process completed in 50 s. Good precision of measurement was obtained (<2 %RSD). Iodate levels were determined using the µPADs in table salt and irrigation water samples. The measured concentrations of the samples correlated well with the values as found by the standard iodometric titration (no statistical difference at a 95% confidence level). The developed device is simple with easy fabrication and rapid operation. It is portable and cost-effective and therefore suitable for quality control of iodate levels in the manufacturing and agricultural industries. [ABSTRACT FROM AUTHOR]

• Agriculture-BERT is compared to other BERT models on agricultural term extraction. • With a few exceptions, Agriculture-BERT performs better than the other BERT models. • Embedding + encoder layers update scores best for extracting terms seen in training. • Embedding layer frozen + encoder layers updated scores best for extracting synonyms. • Embedding layer + top 4 encoder layers updated scores best for novel term extraction. This paper compares different transformer-based language models for automatic term extraction from agriculture-related texts. Agriculture is an important economic sector faced with severe environmental and societal challenges. The collection, annotation and sharing of agricultural scientific knowledge is key to enabling the agricultural sector to address its challenges. Automatic term extraction is a Natural Language Processing task that can provide solutions to text tagging and annotation towards better knowledge and information exchange. It is concerned with the identification of terms pertaining to a domain, or area of expertise, in text and is an important step in knowledge base creation and update pipelines. Transformer-based language modeling technologies like BERT have become popular for automatic term extraction, but limited work has been undertaken so far in applying these methods to agriculture. This paper systematically compares Agriculture-BERT to Sci-BERT, RoBERTa, and vanilla BERT, which were fine-tuned for the automatic extraction of agricultural terms from English texts. The greatest challenge faced in our research was the scarcity of agriculture-related gold standard corpora for measuring automatic term extraction performance. Our results show that, with a few exceptions, Agriculture-BERT performs better than the other models considered in our research. Our main contribution and novelty of the presented research is the investigation of the impact that different language model fine-tuning configuration scenarios had on the term extraction task. More specifically, we tested different scenarios related to the model layers kept frozen, or being updated, during training, to measure the impact they may have on Agriculture-BERT's performance in automatic term extraction. Our results show that the best performance was achieved by: (i) the "embedding layer updated + all encoder layers updated" scenario for the identification of terms also seen during training; (ii) the "embedding layer frozen + all encoder layers updated" scenario for the identification of terms being synonyms to those seen during training; and (iii) the "embedding layer updated + top 4 encoder layers updated" scenario for identifying terms neither seen during training nor being synonyms to those seen during training (novel terms). [ABSTRACT FROM AUTHOR]

Preventing the impact of natural disasters on urban-rural income inequality helps to achieve shared prosperity. This paper uses the functional entropy weight method to calculate the level of natural disasters from 2004 to 2020. Then, this paper discusses the impact of natural disasters on the urban-rural income gap. The empirical results are as follows. (1) Natural disasters have expanded the urban-rural income gap. The regression coefficient is 0.2663, which is significant at the 1% level. (2) In areas with a high proportion of the rural population, the impact of natural disasters is more prominent. (3) Mechanism analysis indicates that natural disasters have widened the urban-rural income gap by restraining rural investment and agricultural production. Finally, this paper suggests strengthening investment in the agricultural sector to mitigate the negative impact of natural disasters. • This paper uses the functional entropy weight method to calculate the level of natural disasters. • This paper quantitatively measures the impact of natural disasters on the urban-rural income gap. • This paper examines the mechanism of the impact of natural disasters on the urban-rural income gap. • This paper provides policy suggestions for narrowing the urban-rural income gap. [ABSTRACT FROM AUTHOR]

• Structural transformation via capital stock increases is enhanced by decreases in transaction cost. • Market policies should therefore complement production policies to facilitate optimal development in the context of structural transformation. • Zambia did not follow standard structural transformation, but successful agricultural led economic growth will be poverty-reducing. The Zambian economy does not follow the standard structural transformation pattern because agriculture's declining share in GDP corresponds to an increase in the share of services and not manufacturing. This paper establishes the relevance of capital as well as transaction costs in structural transformation using a computable general equilibrium (CGE) model. It is found that reducing transaction costs plays a significant role in aiding structural transformation through increases in value-added for all agricultural processing activities in the economy. When capital stock is increased in addition to the transaction cost decreases, the effects are enhanced. This highlights the importance of complementing production policies with market policies to achieve optimal development results. In the combined simulation labour moved out of agriculture to industry with the quantity of labour employed increasing relatively more for the agricultural processing subsector. The paper also confirms a well-known finding that agricultural led economic growth is poverty-reducing. [ABSTRACT FROM AUTHOR]

Triflumuron is an insect growth regulator belonging to the class of insecticides. It finds its primary application in the agricultural sector for the control of various pests, particularly certain beetles, aphids, locusts, and mites. In this paper, a Cu/Fe-BTC composite was synthesized through a straightforward solvothermal approach, subsequently employed as a precursor for the fabrication of an electrochemical sensor denoted as CuFe 2 O 4 @C. The latter exhibited a distinctive core-shell architecture achieved through annealing, and its application was focused on the sensitive detection of triflumuron. The electrochemical behaviour of CuFe 2 O 4 @C for the detection of triflumuron was evaluated by differential pulse voltammetry (DPV) under optimal conditions in wide concentrations from 1.0 × 10−4 to 1.0 × 10−12 mol·L−1, where there existed a superior linear relationship between the current and the concentration of triflumuron with R 2 being 0.995. Meanwhile, CuFe 2 O 4 @C had a low limit of detection (LOD) of 922 fM for triflumuron. The CuFe 2 O 4 @C/GCE exhibited remarkable sensitivity, long-term stability, excellent reproducibility, and repeatability, as well as effective anti-interference capabilities. Additionally, CuFe 2 O 4 @C demonstrated its applicability for the quantitative determination of triflumuron in diverse real samples with commendable recovery rates. In conclusion, the electrochemical sensor not only displayed exceptional performance in terms of stability, reproducibility, and anti-interference, but also showcased its practical utility in quantifying triflumuron across various real-world samples. [Display omitted] • Cu/Fe-BTC was calcined to form porous CuFe 2 O 4 @C with core-shell structure. • CuFe 2 O 4 @C/GCE was constructed for the detection of non-electroactive triflumuron. • CuFe 2 O 4 @C/GCE can quantify triflumuron in real samples. [ABSTRACT FROM AUTHOR]

In the realm of precision agriculture, accurate harvest prediction is vital, as any discrepancies between forecasted and actual yields can lead to significant commercial and logistical challenges. This paper presents a novel deep learning-based approach for detecting and counting tomato fruits using advanced computer vision techniques. Building upon our previously established framework for ultra-wide image acquisition, this approach focuses on a unique patch-cropping technique tailored to tomatoes. This method aligns with the natural clustering of tomatoes, significantly improving object detection in greenhouse settings and thereby enhancing the model's performance in identifying individual fruits. The detection results exhibit a precision of 0.85, a recall of 0.93, and an F1-score of 0.89. Our approach's efficacy is also demonstrated through a case study on harvest prediction in a tomato greenhouse. The proposed methodology exhibited a lower error rate than the agronomist's estimates and proved its practical applicability. These findings suggest that our methodology could substantially contribute to optimizing sustainable farming practices, offering a promising direction for future research and application in the agricultural sector. [Display omitted] • A generalized patch-cropping process for high-resolution images is proposed. • Our patch-cropping technique improves input data for object detection models. • Focus on tomato trusses enhances the identification of small and overlapping tomatoes. • Case study validates improved harvest prediction, better than human estimates. [ABSTRACT FROM AUTHOR]

Clarifying the relationship between new energy and economic growth is an essential prerequisite for achieving the goal of carbon peaking and carbon neutrality. This paper utilizes a combination of nonlinear analysis methods, including transfer entropy, Bai–Perron (BP) structure point breakpoint detection, and complex network theory, to examine the coupling relationship between China's new energy and economy across different stages and sectors. An analysis of the network's topological properties shows that a complex coupling driving relationship exists between the new energy industry and the other 11 economic sectors. In particular, the agricultural sector exerts a significant driving force on the new energy industry, with the most extended duration of influence. Besides, the IT and construction industries also play a driving role in the new energy industry, and the latter's impact is earlier than that of the IT industry. The empirical results can guide the government and makers to formulate long-term sustainable action plans for different economic sectors. • Transfer entropy, BP structure break point detection and complex network are used. • 33 net transfer entropy symbol mode complex networks are constructed. • The coupling relationship between China's new energy and economy is complicated. • The agriculture industry has a large driving force on new energy industry. [ABSTRACT FROM AUTHOR]

This paper aims to demonstrate the feasibility, regarding environmental and economical point of view, of a local tomato farm powered by an offshore wind farm for Romanian Black Sea coast, where there are no such projects implemented and just one is foreseen so far. In this regard, both the agricultural farm and the offshore wind farm were dimensioned and using a new methodology proposed in the year 2023 by the World Bank, an environmental analysis of the project was made with estimating the positive effects that occur in the GHG emissions at the implementation of this project. The relationship between energy inputs and farm yields is analysed for two cycles of tomato crops and a comparative evaluation is conducted to emphasise the favourable influence that the implementation of the proposed solution would exert on the carbon footprint of Romania's agriculture sector. The two scenarios considered are Scenario 1 - S1 with tomato farm in the Schitu area (in greenhouses powered by an offshore wind farm, Cycle 1 and in open field, Cycle 2) and Scenario 2 - S2 when the tomatoes are imported from proximate regions. For both cycles, the energy ratio and energy productivity values were calculated, obtaining 0.11 and 0.13 for cycle C1, and 0.67 and 0.84 for cycle C2, respectively. The benefits of the proposed solution presented in S1 are represented by a) enhancing agricultural production by exploiting a renewable energy source with a yearly production of 45.07 GWh; b) reducing the transport distance of tomatoes by 324,057 km; c) promoting the local economy by employing an average of 124 people; d) improving the carbon footprint of Romania's agricultural sector with a reduction of CO2e per year of 43.13%. [Display omitted] • Local tomato farm located in Romania, powered by an offshore wind farm. • Improving the safety and resilience of the food system. • Reduction of the carbon footprint of Romania's agriculture sector. • Boost of local economy. • Relatively quick return of investment. [ABSTRACT FROM AUTHOR]

• Adopting ecological agricultural technologies is an effective way to achieve sustainable agriculture. • The ecological effect of digital economy empowerment should be emphasized. • We analyze how farmers' participation in the digital economy affects their adoption of ecological agricultural technologies. • The treatment effect model is used to address the endogeneity issue. • Farmers' participation in the digital economy can promote ecological agricultural technologies adoption. Adopting ecological agricultural technologies (EATs) is an effective way to guarantee food security and protect the environment. With the advancement of the digital village strategy, the continuous integration of the digital economy with the rural economy has gradually become an emerging driving force for sustainable agriculture. However, few studies have examined the impact of farmers' participation in the digital economy in different links of the agricultural industry chain on EATs adoption from a micro perspective. Using data from 2,825 grain farmers from the 2020 China Rural Revitalization Survey, this paper applies the treatment effect model and the mediating effect model to empirically analyze the impact and mechanism of participation in the digital economy on farmers' EATs adoption from an ecological effect perspective. This paper characterizes farmers' participation in the digital economy in terms of digital production, digital sales, and digital finance. The results show that participation in the digital economy significantly promotes farmers' EATs adoption. The sub-dimensional test found that participation in digital production, digital sales, and digital finance significantly promotes farmers' EATs adoption. The above findings remain robust after a series of robustness tests, such as changing the estimation method and replacing the dependent variable. Mechanism analysis shows that participation in the digital economy, especially digital sales and digital finance, could promote farmers' EATs adoption by increasing the demand for agricultural machinery services, improving information availability, and enhancing food safety cognition; participation in the digital production could promote farmers' EATs adoption by increasing the demand for agricultural machinery services. Results of heterogeneity analysis show that the promotion effect of participation in the digital economy on EATs adoption is more substantial among farmers with higher digital literacy and larger cultivated land scales. The findings of this paper provide insights for China and other countries to effectively use digital technology to promote sustainable agriculture under the pressure of achieving sustainable development goals. [ABSTRACT FROM AUTHOR]

In field robotics, particularly in the agricultural sector, precise localization presents a challenge due to the constantly changing nature of the environment. Simultaneous Localization and Mapping algorithms can provide an effective estimation of a robot's position, but their long-term performance may be impacted by false data associations. Additionally, alternative strategies such as the use of RTK-GPS can also have limitations, such as dependence on external infrastructure. To address these challenges, this paper introduces a novel stability scan filter. This filter can learn and infer the motion status of objects in the environment, allowing it to identify the most stable objects and use them as landmarks for robust robot localization in a continuously changing environment. The proposed method involves an unsupervised point-wise labelling of LiDAR frames by utilizing temporal observations of the environment, as well as a regression network, called Long-Term Stability Network (LTS-NET) to learn and infer 3D LiDAR points long-term motion status. Experiments demonstrate the ability of the stability scan filter to infer the motion stability of objects on a real agricultural long-term dataset. Results show that by only utilizing points belonging to long-term stable objects, the localization system exhibits reliable and robust localization performance for long-term missions compared to using the entire LiDAR frame points. [ABSTRACT FROM AUTHOR]

Achieving greenhouse gas emission reductions to meet the 1.5 °C target by 2050 is particularly challenging for the agricultural sector. Cleaner Growth Mitigation Measures (CG-MM) are practices and technologies which balance food production with greenhouse gas abatement and are also identified as being economically acceptable. This paper explores a large number of CG-MMs to assess their feasibility using a novel participatory filtering process. Each measure is explored through a series of mapping exercises with supply chain actors to identify the impact on greenhouse gases and their applicability to different farming systems. These are then refined in a series of farmer workshops to identify which measures were considered feasible. Results show that acceptance of CG-MMs by the industry and the farmers themselves is limited. A pessimistic estimate of 50–60% of potential abatement could be lost due to lack of acceptance of currently available CG-MMs within farming. This impacts expectations on decarbonisation trajectories for the agricultural sector to reach net zero by 2050. This also argues for targeted approaches in order to capture some of the lost abatement. • Cleaner growth mitigation measures are assessed at farming systems level. • Half of the potential abatement from cost-effective measures is considered feasible for farmers. • Agricultural support needs to be targeted to capture lost abatement from cleaner growth measures. [ABSTRACT FROM AUTHOR]

Highlights • We present a systematic review of the literature on FMISs since 2008. • We reviewed 38 papers out of 1051 identified studies. • We identified 81 unique features of FMISs. • We identified 53 unique obstacles for FMISs. • We ranked the features and obstacles and discussed the key lessons learned. Abstract Various Farm Management Information Systems (FMISs) have been developed to support the management of the farm businesses. These FMISs typically support the different domains of the agricultural sector, such as arable and dairy farming; and include different set of features, such as crop, field, and financial management. These FMISs also have to deal with diverse obstacles during their development and adoption, such as lack of standardized data, cost and usability. Though several papers have been published in the past several years on this topic, there has been no explicit attempt to systematically review these papers to identify and characterize the features and obstacles. The objective of this study is to identify and describe the state-of-the-art of FMISs and as such pave the way for further research and development of FMISs. We applied a systematic literature review protocol in which we included the literature published from 2008 to 2018. We found 1048 papers of which 38 papers were selected as primary studies that we analyzed further in detail. From the detailed analysis, we identified 81 unique FMIS features and 51 unique obstacles of FMISs. We have systematically ranked the identified features and obstacles and describe the key associated aspects. These aspects include the agricultural domains, modeling approaches, delivery models, and identified stakeholders. [ABSTRACT FROM AUTHOR]

Much of the existing literature on greenhouse gas peaking focuses on carbon dioxide (CO 2) emissions, however there is a lack of research on peaking methane (CH 4) emissions, the second-largest greenhouse gas. This paper attempts to determine and classify the peaking status of anthropogenic CH 4 emissions in 188 economies over the period 1970–2021 using a trend analysis model and a decoupling model. Fifty-eight economies achieved a peak in total CH 4 emissions, and 125 economies achieved a peak in CH 4 emissions per capita, showing marked spatial-temporal variation. Emissions generated by the agricultural sector peaked in 84 economies, followed by the energy sector in 63 economies, and the waste sector in 34 economies. Of these, the most volatile was the peak in emissions in the energy sector. There were 45 benign peaking economies and 13 pseudo-peaking economies. The majority of developed economies showed strong decoupling after reaching their peaks. The agricultural sector had the most peak economies which achieved strong decoupling. Among the factors influencing national pathways to peak CH 4 emissions, technological progress driving CH 4 emissions reductions in benign peak economies is the most important. This study provides targeted strategies for global and national CH 4 emissions reduction, particularly given the historical trend and current profile of economies at different levels of emissions evolution. • Peaking status of CH 4 emissions in 188 economies during 1970–2021 is evaluated. • 58 economies peaked in total CH 4 emissions and 125 in per capita emissions. • Agriculture-, energy- and waste-related emissions show various peaking patterns. • There are 45 benign peaking economies and 13 pseudo-peaking economies. • Factors influencing national pathways to peak CH 4 emissions are identified. [ABSTRACT FROM AUTHOR]

The eradication of illicit drug crops in developing countries through international agricultural cooperation in alternative livelihoods is a new mode of clean production. This paper examines the market-oriented alternative livelihood (AL) model implemented by Chinese agricultural companies in Laos, focusing on land-based cooperation between these companies, local authorities, and communities. It aims to reveal the conditions for successful land-based cooperation and mutual trust building with local communities and government agencies. Three Chinese companies involved in alternative cultivation projects in Laos are selected as case studies. The findings highlight the market-oriented AL model as a beneficial approach to transition from illicit crop cultivation to clean production. The study emphasizes the importance of land-based cooperation and trust building for the sustainable operation of market-oriented alternative livelihoods. Challenges faced by the companies include managing international market risks and developing locally-led technical and management teams. The research underscores the need for inclusive innovation in land-based cooperation and alternative livelihoods in the Global South. It calls for further exploration of innovative approaches to inclusive cooperation, local empowerment, and capacity building. The study contributes to understanding the potential of international agricultural cooperation in promoting alternative livelihoods and sustainable development in illicit crop-growing regions. [ABSTRACT FROM AUTHOR]

• Detailed review of over 300 research papers. • Object Detection and Tracking are suitable techniques to enhance Precision Farming. • Critical discussion regarding the challenges of Object Detection and Tracking. • Potential research lines to improve Object Detection and Tracking are identified. Object Detection and Tracking have gained importance in recent years because of the great advances in image and video analysis techniques and the accurate results these technologies are producing. Moreover, they have successfully been applied to multiple fields, including the agricultural domain since they offer real-time monitoring of the status of the crops and animals while counting how many are present within a field/barn. This review aims to review the current literature on Object Detection and Tracking within the field of Precision Farming. For that, over 300 research articles were explored, from which 150 articles from the last five years were systematically reviewed and analysed regarding the algorithms they implemented, the domain they belong to, the difficulties they faced, and which limitations should be tackled in the future. Lastly, it examines potential issues that this approach might have, for instance, the lack of open-source datasets with labelled data. The findings of this study indicate that Object Detection and Tracking are critical techniques to enhance Precision Farming and pave the way for robotization for the agricultural sector since they provide accurate results and insights on crop and animal management, and optimize resource allocation. Future work should focus on the optimal acquisition of the datasets prior to Object Detection and Tracking, along with the consideration of the biophysical environment of the farming scenarios. [ABSTRACT FROM AUTHOR]

• Ethiopia implemented widespread agricultural inteventions in the past decades. • We estimate the yields and food security impacts of the interventions implemented by the Ethiopian Agricultural Transformation Agency (ATA) between 2011 and 2016. • We combine project and remote-sensing data with a nationally representative household panel survey. • Our identification strategy exploits the temporal and geographic variation in the roll-out of the ATA interventions. • Results show that large-scale agricultural interventions are associated with higher agricultural yields and better food security outcomes. In the past decades, the Government of Ethiopia implemented widespread interventions with the objective of modernizing the agricultural sector and stimulating transformative growth. This paper assesses the agricultural yields and food security impacts of those interventions. Combining project and remote-sensing data with a nationally representative household panel survey, we identify impacts by exploiting the temporal and geographic variation in the roll-out of the interventions implemented by the Ethiopian Agricultural Transformation Agency from 2011 to 2016. Results show that large-scale agricultural interventions are associated with higher agricultural yields and better food security outcomes. These outcomes underscore the linkages that persist between agricultural development and food security for rural households living in mostly agrarian economies and the potential for systems-wide interventions to catalyze agricultural transformation. [ABSTRACT FROM AUTHOR]

This paper presents a study focused on the analysis of phytosanitary treatment sales in the Souss Massa region of Morocco. The objective of the study is to predict the sales of agricultural products, particularly crop protection solutions, aiming to optimize supply chain operations and meet customer demand effectively. Data for this study are collected from multiple sources, including the Enterprise Resource Planning (ERP) system called Microsoft Dynamics AXAPTA used by a leading agricultural company operating in the region. Information such as the date of sale, farming type, climate, and specific sales locations within the Sous Massa region is gathered. Machine learning techniques are applied for forecasting. Various regression models, including the Gradient Boosting Regressor algorithm, are employed to determine the most accurate predictor. Evaluation of the models reveals promising results, with a Mean Absolute Error (MAE) of 0.0035 and a Root Mean Square Error (RMSE) of 0.0066. The results obtained by applying various regression models, including the Gradient Boosting Regressor algorithm, demonstrate promising prediction scores. These findings contribute to the field of sales prediction in the agricultural industry while considering the impact of climate conditions, farming practices, and regional factors. [ABSTRACT FROM AUTHOR]

Abstract: To publish papers that have impact is indispensable for good scientists. It is frequently accepted that the impact factor (IF) of a journal reflects the likelihood of a paper to have impact. Many scientists may then attempt publishing their papers in journals with the highest possible IF. This is relevant for crop scientists as in many cases our hypotheses may be relevant to agronomy/agriculture but the product of our work may be published in the more basic plant science journals, which in general have higher IF than agronomic journals. To test whether agronomic journals may be better vehicles than plant science journals for crop physiological work, I did an analysis of my own publications (only “standard” papers, disregarding reviews) relating their annual citation rates against the IF of the journal in which they were published. The relationship was virtually inexistent, and my most frequently quoted papers were published in agronomic rather than in plant sciences journals. Conversely, restricting the analysis to the papers published within agronomic journals there was a relationship between the record of citations of the papers and the IF of the journals, particularly if the analysis is restricted to the most quoted papers in each journal. I then analyzed the cases of three other well-recognized crop scientists, to check whether the findings with my results were exceptional, and in the three cases results were remarkably similar. Therefore, it seems that the journal IF might be a valuable criterion for choosing the best journal in which to publish our best contributions only when comparing within agronomic journals, but not within combinations of agronomy and plant science journals. This should be also taken into account in evaluation processes when comparing scientists working in related but not identical fields. [Copyright &y& Elsevier]

This paper investigates the decoupling of carbon dioxide (CO 2 ) emissions from agricultural economic growth in 30 Chinese provinces from 1997 to 2014. First, CO 2 emissions from agricultural production activities, farming, and livestock husbandry are computed. Then, this paper explores the spatial and temporal heterogeneity of CO 2 emissions and their intensity in China’s agricultural sector. Finally, this paper compares decoupling of China’s agricultural CO 2 emissions from agricultural output value across different regions and years. The results indicate that fertilizer, in-season rice cultivation, and cattle generated the most CO 2 emissions in the categories of agricultural production activities, farming, and livestock husbandry, respectively. The results also show that East China covered more periods of the strong decoupling of CO 2 emissions from agricultural output value between 1997 and 2014. To decrease CO 2 emissions, this paper suggests imposing a higher value-added tax on fertilizer producers, while waiving value-added tax for organic fertilizer and substituting animal manure for conventional fertilizers where possible. [ABSTRACT FROM AUTHOR]

This study investigates the potential use of magnesium based phosphate cement prepared from a heavy magnesium oxide and monopotassium phosphate as a binder for the production of composite panels using bio-based industrial residues such as bagasse, hemp hurds, pine sawdust, paper mill sludge and wastepaper as raw materials. These residues were used to produce light-weight and durable materials that can compare with current Portland cement based products. The phosphate binder is fast setting, cold curing and has a low carbon footprint compared to its Portland cement counterpart. The development of phosphate bonded board products promises to reduce the energy requirements in the manufacturing process of board products, and also provides an alternative route for disposal or value addition to bio-based residues by developing environmentally friendly products. The board manufacturing process was laid out on a central composite design (CCD) to model the response variable, utilizing as much residues as technically feasible. The design allowed for the production of low and medium density boards that can be used for non-structural interior finishes and partition boards. Response surface methodology (RSM) was used to show the relationships between the production variables and predict the board property by variable optimisation. Tests of mechanical and physical properties were conducted on the boards. The density of hemp boards ranged from 0.59–0.83 g/cm 3 , bagasse boards ranged from 0.54–0.78 g/cm 3 , pine boards ranged from 0.58–0.84 g/cm 3 , paper sludge boards ranged from 0.68–0.81 g/cm 3 and wastepaper boards ranged from 0.67–0.81 g/cm 3 . The study has shown that it is feasible to produce phosphate based board products using bio based industrial and agricultural residues. The physical properties of the products met the minimum requirements for cement bonded particleboard (EN 634:2007) and LD-1 grade particle board (ANSI 208.1:1999). [ABSTRACT FROM AUTHOR]

Agricultural production struggles with low efficiency and high emissions. This urgently calls for a collaborative effort between government and non-government organizations in environmental governance and policy implementation. In order to explore the supply-shock, demand-induced or superposition effects of the formal and informal environmental regulations to agricultural total factor productivity, using listed Chinese agricultural enterprise data from 2003 to 2018 as a sample, this paper employed a two-way fixed difference-in-difference model to analyze from supply and demand perspective. The results indicate that, (1) formal environmental regulations and environmental non-governmental organizations not only drive total factor productivity caused by dynamic changes on the supply and demand sides respectively, but also exhibit strong policy superposition effect. (2) The Analysis of policy intensity heterogeneity illustrated that the intensity of command-control and market-incentive environmental regulations could exert impact on the direct dual policies' effects. (3) The mechanism test found that green innovation and market forces play a mediating or regulating role. This paper provides empirical evidence for strengthening the policy orientation of synergistic governance, and has certain reference significance to realize the rise of green agriculture-supporting economy. [Display omitted] • Analysis is based on the supply and demand sides, with DID method mainly. • Heterogeneous environmental regulations respectively stimulate total factor productivity. • Dual regulations could generate a superposition effect. • Policy effects are affected by the heterogeneity across policy intensity. • Green innovation and market forces have regulating or mediating effects. [ABSTRACT FROM AUTHOR]

Agricultural production struggles with low efficiency and high emissions. This urgently calls for a collaborative effort between government and non-government organizations in environmental governance and policy implementation. In order to explore the supply-shock, demand-induced or superposition effects of the formal and informal environmental regulations to agricultural total factor productivity, using listed Chinese agricultural enterprise data from 2003 to 2018 as a sample, this paper employed a two-way fixed difference-in-difference model to analyze from supply and demand perspective. The results indicate that, (1) formal environmental regulations and environmental non-governmental organizations not only drive total factor productivity caused by dynamic changes on the supply and demand sides respectively, but also exhibit strong policy superposition effect. (2) The Analysis of policy intensity heterogeneity illustrated that the intensity of command-control and market-incentive environmental regulations could exert impact on the direct dual policies' effects. (3) The mechanism test found that green innovation and market forces play a mediating or regulating role. This paper provides empirical evidence for strengthening the policy orientation of synergistic governance, and has certain reference significance to realize the rise of green agriculture-supporting economy. [Display omitted] • Analysis is based on the supply and demand sides, with DID method mainly. • Heterogeneous environmental regulations respectively stimulate total factor productivity. • Dual regulations could generate a superposition effect. • Policy effects are affected by the heterogeneity across policy intensity. • Green innovation and market forces have regulating or mediating effects. [ABSTRACT FROM AUTHOR]

Thin-walled cylindrical steel silos are one of the key structures for storage of materials in many industries and agricultural sectors. They are susceptible to instability under wind pressure when they are empty or partially filled. This paper investigates numerically the wind buckling behavior of three sample steel silos with stepped walls composed of isotropic rolled shells. Wind load vertical and circumferential distributions were adopted from Eurocodes. Two proposed circumferential pressure distributions for an isolated silo and a silo in a group with a closed roof were taken into consideration. Moreover, the effect of additional inward pressure, proposed by Eurocode, on buckling capacity of vented silos with a small opening was evaluated. Accordingly, comprehensive 3D finite element models were used and detailed linear and non-linear buckling analyses were conducted. The wind buckling capacity of sample structures considering multiple amplitudes of initial imperfections was also assessed. The results obtained suggest a considerable decrease in the buckling resistance of imperfect silos. Finally, Eurocode provisions for the wind buckling stress design of unstiffened cylinders with step-wise variable wall thickness were examined and the relevant conclusions have been made. Image 1 • Comprehensive FEM wind buckling assessments of three steel silos were conducted. • Wind distributions for an isolated silo and a silo in a group were considered. • Valuable insights into wind buckling of closed roof and vented silos were provided. • Extensive imperfection sensitivity analyses were undertaken. • Discharge buckling assessments of sample silos by GMNIA analysis were presented. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the ways in which a group of scientists in Edinburgh worked across mice and sheep during the last quarter of the twentieth century. With this local episode, we show the utility of an interspecies perspective to investigate recent historical transformations in the life sciences. We argue that the emergence of animal biotechnology was the result of interactions between neoliberal policymakers, science administrators, molecular biologists, agricultural breeders, and the laboratory and farm organisms with which they worked. During the early 1980s, all these actors believed that the exportation of genetic engineering techniques from mice to farm animals would lead to more effective breeding programmes in the agricultural sciences. However, the circulation of people, money, expertise and infrastructures that the experiments required, as well as the practical constraints of working with mice and sheep, resisted a simple scaling-up from one organism to the other. This displaced the goals of the Edinburgh scientists from the production of transgenic sheep to stem cell research and human regenerative medicine. We account for this unexpected shift by looking at the interplay between science policy and its implementation via collective action and bench work across different organisms. The emergence of animal biotechnology in Edinburgh also provides historiographical insights on the birth of Dolly the sheep and, more generally, on the interactions between the molecular and the reproductive sciences at the fall of the twentieth century. • The work of scientists across mice, sheep and other species provides a new lens to look at the history of biotechnology. • Interspecies work enables historians to think differently about the interactions between developmental and molecular biology. • Agricultural biotechnology stemmed from the collective action of different people, animals, institutions and places. • Animals were as important as human actors in the configuration of agricultural biotechnology. • The birth of Dolly was a contingent product of research to produce genetically modified – rather than identical – sheep. [ABSTRACT FROM AUTHOR]

• Private-sector initiatives can benefit smallholder farmers. • Users of tractor services almost doubled their income by cultivating more land. • Yields increased but income per hectare did not increase significantly. • Demand for hired labor increased due to land expansion and a reduction of family labor. After years of neglect, there is a renewed interest in agricultural mechanization in Africa. Since government initiatives to promote mechanization are confronted with major governance challenges, private-sector initiatives may offer a promising alternative. However, given limited scientific studies on such private-sector options such approaches are often viewed skeptically. One concern is that multi-national agribusiness companies take advantage of smallholder farmers. Another concern is that mechanization causes rural unemployment. To shed light on these concerns, this paper analyzes an initiative of the agricultural machinery manufacturer John Deere to promote smallholder mechanization in Zambia through a contractor model. The analysis focuses on the impact of this initiative on farmers who receive tractor services using Propensity Score Matching. The results indicate that farmers can almost double their income by cultivating a much larger share of their land. The analysis suggests that the increased income is used for children's education and more food, but does not result in increased food diversity. The demand for hired labor increases due to land expansion and due to a shift from family labor, including that of children, to hired labor. Questions that require further investigation are identified, including strategies to incentivize tractor owners to provide services, to also increase land productivity, and to avoid new forms of dependency of agricultural laborers that may result from a shift in the timing of the labor demand. [ABSTRACT FROM AUTHOR]

Abstract The study presents possibilities to use biodegradable waste, formed in the farms and agro industry, sewage sludge and waste from food plants and trade, for energy generation. Farms were evaluated by relative size and volumes of produced biodegradable waste as well as by type. Also, the waste volumes, efficiency of biogas generation technologies processing various biodegradable organic waste and their mixes were investigated. Farms and agro industry companies gain environmental and economic benefit while implementing the described technologies. It was defined that organic waste from small livestock farms are dissipated sources of pollution. It is technically difficult to assess their impact on the environment, especially its liquid fraction, thus more attention and means should be paid for reduction of this impact. Therefore, the paper also investigates trends defined in Lithuania's agriculture strategy with regard to increase feasibility of farms. The evolution of the Lithuanian energy sector trends and the use of renewable energy sources for energy production growth are given. The development of the biogas production is analysed using the different sources of biodegradable waste. It is found that in Lithuania last year, production of biogas from biodegradable waste show strong growth from 2 thousand tonnes oil equivalent (ktoe) in 2006 to 32.1 ktoe in 2016. Fact-findings disclosed that biogas can replace certain share of conventional fuel for production of heat and power, and it can also be used in transport sector. The biodegradable waste resources and possibility to use it for biogas production have been determined in the country and main factors affecting GHG emission and pathways to reduce it have been discussed. The data given in the investigation can be applied in other countries aiming to reduce conventional fuels use and improve environmental conditions. Highlights • Policy of biogas generation and use in EU and Lithuania are discussed. • Energy sector and use of biogas in Lithuania is analysed. • Biodegradable waste resources for biogas production in Lithuania are studied. • Influence of biodegradable waste on environmental impact is analysed. [ABSTRACT FROM AUTHOR]

Highlights • A stochastic optimization model for sugarcane supply chain planning is proposed. • The model integrates sowing, growing and harvesting operations. • Strategic and tactical decisions are simultaneously considered. • Financial feasibility for a new agro industrial project is assessed. Abstract The aim in new agricultural investment projects is to achieve a proper balance between customer value creation and investor economic benefits. Several authors have highlighted the importance of integrating agroindustrial supply chain operations, as a way to improve competitiveness. In the specific case of sugarcane, most research only integrates harvest and transport operations, lacking simultaneous analysis of sowing, growing and harvesting. Also, from the perspective of sugarcane supply chain planning, few contributions undertake tactical and strategic decisions. This paper proposes an optimization model for sugarcane supply chain planning, integrating several agricultural decisions from a strategic-tactical planning perspective. The uncertainty effects generated by weather conditions were also considered. As a main contribution, the model establishes a set of agricultural decisions that maximize cane yield, in order to optimize the Net Present Value (NPV) of expected profits. The proposed model is utilized to evaluate the feasibility of a new biofuel production plant in Colombia. Results allowed for the identification of critical variables to control, in order to reduce investment risk. [ABSTRACT FROM AUTHOR]

Highlights • Modeling citrus knowledge based on the Chinese Eight-Point Charter of Agriculture. • A knowledge framework to facilitate collaborative agriculture ontology development. • A high-quality citrus Fertilization OWL ontology. Abstract Developing large-scale agricultural ontologies is a challenging and error-prone task that requires substantial effort and collaboration between domain experts and ontology developers due to the complexity of agricultural knowledge. Inspired by the Chinese Eight-Point Charter of Agriculture, i.e., Soil, Fertilization, Water, Variety, Density, Protection, Management and Tool, this paper presents an approach to modeling and integrating citrus production knowledge. Citrus domain knowledge is classified into eight categories based on the Eight-Point Charter of Agriculture, and the relationships in each category and among categories are established. The eight categories and the relationships are defined as the citrus production knowledge framework. Then, we propose mechanisms to develop citrus ontology based on the citrus production knowledge framework. The Fertilization ontology is created as an illustration of our approach, which contains 866 ontology entities and 12,583 Resource Description Framework triples. The structural evaluation results of the eight metrics for the Fertilization ontology are considerably better than the average and median values of 1413 Web ontologies. In addition, four antipatterns were used to evaluate the ontology, and no occurrence of the antipatterns was detected for the 866 ontology entities. The accuracy of the ontology is ensured by the competency evaluation of the 110 questions with 88% accuracy. Our approach provides an effective solution for modeling complex agricultural knowledge and transforming the agriculture domain knowledge into computable resources. [ABSTRACT FROM AUTHOR]

Highlights • A decision support tool for farmer's strategic thinking. • An activity concept to multi-criteria and multi-scale analyses. • Long rotations increased income but also labor needs. • Crop contracts contributed to the appearance of weeds resistance. Abstract In an uncertain socio-economic and climatic context, sustainable farming is a major challenge for farmers as well as for their agricultural advisors. It is therefore essential to develop a decision support tool (DST) that is likely to be useful to establish and evaluate new production strategies, in accordance with farm sustainability and environmental protection. This paper aims, by using the Agricultural Activity concept, to put forward and test a DST based on mathematical programming used to evaluate strategic production decisions, in conjunction with farmers and agricultural advisors. In the test case, the decisions concern the crop activities and their spatio-temporal combinations in order to reduce both the use of herbicides and the risk of weed resistance to herbicides in cereal-based production systems. Moreover, the DST considers the availability of the workforce during the crop cycle to determine the periods which are likely to require the most significant increase in comparison to the current situation. One scenario showing the current situation (Sc_baseline) and two alternative scenarios have been defined to address the weed-herbicide issue. The comparison of the scenario promoting soil tillage and the introduction of spring crops with Sc_Baseline has shown that the adoption of long-term rotations, the increase in winter crop frequency and the return to deep soil tillage have contributed to an increase in farmer income, total labor and water consumption by 7, 21 and 22% respectively. However, the intensity of pesticide use and nitrate fertilization have dropped by 15% and 17% respectively. By allowing the farmer to establish specific contracts for certain crops, the average income as well as the use of pesticide and nitrate fertilization were increased by at least 10%. This situation is the result of a simplification of rotations with a predominance of winter cereals and the elimination of deep soil tillage. The analysis of these scenarios shows that the use of the DST has made it possible not only to put forward and evaluate alternatives that result in strategic decisions but also to understand, with the concept of Agricultural Activity, the biophysical and technical processes relating to farmer decisions and their impacts at field and farm level. Understanding and sharing this functional chain at farm level is expected to strengthen the farmer-advisor relationship in order to address the complex challenges of farming system sustainability. [ABSTRACT FROM AUTHOR]

The causal relationship between land tenure security and agricultural investment plays a crucial role in the process of rural development. Land property rights are key determinants for reducing poverty through agricultural productivity, natural resource management, and socio-economic inequalities. Growing competition for land access increases the sensitivity in land socio-economic stakes and related policies in developing countries. This paper analyzes the impacts of land rights on land-related investment in rural Burkina Faso. We use rural representative panel data and appropriate methods. The Two-Stage Conditional Maximum Likelihood approach is used for dealing with endogeneity, while the Generalized Structural Equation Model allows us to control for random effects. We show that stronger land rights increase land-attached investment, and land investment has positive and negative effect on stronger and weaker land rights, respectively, in rural Burkina Faso. Our study also finds that women and immigrants have less land rights than men and natives, respectively. Our contribution to related literature includes the use of rigorous process of hierarchy in land rights. The close link of the results with land right strength is of great interest for cautious practitioners in land tenure and investment. Moreover, our results imply that a gain of efficiency can be achieved from combining practices in land tenure and investment. [ABSTRACT FROM AUTHOR]

Managing organic waste streams is a major challenge for the agricultural industry. Anaerobic digestion (AD) of organic wastes is a preferred option in the waste management hierarchy, as this process can generate renewable energy, reduce emissions from waste storage, and produce fertiliser material. However, Nitrate Vulnerable Zone legislation and seasonal restrictions can limit the use of digestate on agricultural land. In this paper we demonstrate the potential of cultivating microalgae on digestate as a feedstock, either directly after dilution, or indirectly from effluent remaining after biofertiliser extraction. Resultant microalgal biomass can then be used to produce livestock feed, biofuel or for higher value bio-products. The approach could mitigate for possible regional excesses, and substitute conventional high-impact products with bio-resources, enhancing sustainability within a circular economy. Recycling nutrients from digestate with algal technology is at an early stage. We present and discuss challenges and opportunities associated with developing this new technology. [ABSTRACT FROM AUTHOR]

Considering global water scarcity, especially in the agricultural sector, many attempts are being made to improve water productivity and save more water. Changing irrigation systems from surface to pressurized has been one of the considerable actions in this regard. However, any attempts to do so must be evaluated in an integrated perspective at the basin level by means of relevant agro-hydrological models before making them operational. This paper aims to address this issue by using the soil and water assessment tool (SWAT). Zarrineh Rud River, the main feeding river of northwest Iran’s Lake Urmia, is selected as the case study for exploring this methodology. This lake has been shrinking since 1995, and to save water and increase the basin’s water productivity, improvement of pressurized systems is considered as a solution. SWAT cannot directly simulate changing the irrigation system from surface to pressurized. In this study, we use the innovative approach of applying the model to simulate different irrigation systems considering real irrigation management variables such as depth and date of each irrigation event. Also, in order to improve its performance in simulation of such systems, a comprehensive calibration procedure was used based on a wide range of data for hydrological and agricultural variables. The results showed that changing the current irrigation to a pressurized system can increase water productivity up to 15% due to increases in crop yield, better water distribution and greater actual evapotranspiration. However, pressurized irrigation results in no significant change in total inflow to the lake. Notably, these systems can intensify drawdown of the basin’s water table up to 20%. So, any significant “real water saving” program in the basin must be associated with reduction of evapotranspiration by adopting measures like reducing cultivated areas, changing cropping patterns to less water consuming plants, or applying deficit irrigation. The applied methodology of this paper as well as the comprehensive calibration and setup of the SWAT model with the readily available hydrological and agricultural variables can be a good sample for similar works. However, further work is still needed to more broadly test this approach in areas with intensive irrigation systems. [ABSTRACT FROM AUTHOR]

Public-private partnerships are crucial for advancing agricultural sustainability and tackling issues related to enhancing global food security. They help make technology accessible to farmers so they can access markets. As PPPs bring together participants from the public, private, and civil society, they are commonly touted as a means of boosting productivity and fostering growth in the agriculture and food sectors. The PPP can assist in implementing cutting-edge technological breakthroughs and promoting private sector participation to reduce risks that could otherwise be excessive. PPPs are commonly understood as having the potential to modernize the agriculture industry and offer numerous concessions to help farmers achieve sustainable agricultural growth. The objective of the present study is to understand how these new partnerships are expected to play significant roles in identifying answers to the most important agricultural problems that Indian Agriculture is confronting. The study further aims to understand the critical issues in PPPs in the agriculture sector in the Indian context by analyzing their interrelationships and prioritization. The study utilizes Grey DEMATEL to determine these contextual relationships for PPP issues. Grey systems theory is a methodology that incorporates improbability and vagueness into the analysis. The critical issues in agricultural PPPs, as identified in the study, are "Complex and Time-Consuming Procedures", "Governance Issues", "Lack of Enabling Environment", "Costly Contracting and Endogenous Contract Incompleteness", and "Coordination Failures". The present paper makes an effort to provide a detailed and exhaustive assessment of difficulties in agriculture PPPs in India, as well as a path forward for policymakers. • Analysing the key issues in PPPs in agriculture in the Indian context. • Interrelationships and prioritization of these issues. • Using the Grey DEMATEL to determine these contextual relationships for PPP issues. [ABSTRACT FROM AUTHOR]

Lignocellulosic crops like kenaf, flax and hemp have been extensively studied the last years with the help of new technology and many new products fabricated with them are ready for the market or have already been marketed. Both the core material and the bast fibers of these crops are ideal feedstocks for the production of yarns and paper of high quality, for rendering flame retardant properties to composites reinforced with them, for the production of energy via a sustainable way without polluting the environment and for use as alternative materials to wood for the manufacturing of panels like particleboards and fiber boards (MDF, HDF, etc.). The investigation of lignocellulosic feedstock as potential source for the sustainable production of materials, products and energy has resulted in promising solutions for the successful replacement of their counterparts made from fossil raw materials. However, the related industries still have some challenges to face, like the cost of materials from lignocellulosic plants in comparison with those derived from fossil sources and the availability of the feedstock throughout the year. [ABSTRACT FROM AUTHOR]

Ammonia emission in the atmosphere is a major concern in EU countries. In 2005 it was reported by European Environmental Agency (EEA) that 93% of the EU15 countries come from agricultural sector. This paper reported an open-path differential optical absorption spectroscopy (DOAS) system to monitor ammonia emission in the agricultural sector. In the initial stage, analysis of the absorption lines for ammonia in the ultraviolet region was performed and the optimum wavelength for ammonia measurement was selected at 212 nm. A few tests were conducted to verify the detection system reliability and this DOAS system was able to detect various ammonia concentrations from 0 to 100 ppm. It is also shown that this DOAS system has better response and recovery time than a chemisorption sensor. In this paper, ammonia levels measurement in a cattle barn for initial monitoring purpose is also reported. The DOAS system is able to measure ammonia concentrations of 2 ppm with fast response time (4 s). [ABSTRACT FROM AUTHOR]

Rapid development of agriculture mechanization and the agro–industry in China (considered as a large agricultural country) has led to a substantial increase in energy consumption and CO 2 emissions. Majority of existing studies usually explore the driving forces of the sector's CO 2 emissions using the averaging method. However, data distribution of economic variables is often non–normal, with the tail having hidden important information. According to the average annual CO 2 emissions, this paper divides China's 30 provinces into six quantile grades, and uses the quantile regression method to investigate the driving forces of CO 2 emissions under high, medium, and low emission levels. The results show that the effects of economic growth on CO 2 emissions in the upper 90th and 75th–90th quantile provinces are higher than in the 50th–75th, 25th–50th, 10th–25th and lower 10th quantile provinces due to the differences in fixed–asset investment and agricultural processing. The impact of energy efficiency in the upper 90th, 75th–90th, and 50th–75th quantile provinces are stronger than those in the 25th–50th, 10th–25th, and lower 10th quantile provinces because of the huge difference in R&D funding and R&D personnel investments. The effect of urbanization in the higher 90th quantile provinces is higher than in the other quantile provinces, owing to the differences in the level of agricultural mechanization and human capital accumulation. Similarly, financial capacity has the largest impact on CO 2 emissions in the upper 90th quantile provinces in all quantile provinces. However, the impact of industrialization in the upper 90th quantile provinces is lower than in other quantile provinces. Thus, the heterogeneous effects of the driving forces should be taken into consideration when discussing CO 2 emissions reduction in China's agriculture sector. [ABSTRACT FROM AUTHOR]

In recent years, interest has emerged in policy circles and among academics about the use of foreign aid to reduce international migration. Scholars have investigated this aid-migration nexus, but results have been mixed and questions remain. This paper contributes to the literature by comparing the effects of rural and urban development aid on international migration. Specifically, we hypothesize that while increases in rural development aid to developing countries reduce emigration from those countries, greater urban aid produces the opposite effect – higher rates of emigration. These hypotheses are informed by two theoretical mechanisms. The first mechanism focuses on the divergent preferences of rural and urban populations regarding emigration. Aid targeting these respective populations provides each with resources to follow through on their migratory ambitions, or lack thereof. The second mechanism focuses on contrasting impacts of rural and urban aid on agricultural sector development and the effects of this sort of development on emigration. We analyze cross-national time series data to test our hypotheses regarding rural and urban development aid, finding that countries that receive larger amounts of rural development aid have lower emigration rates. Then we turn to survey data from the Arab Barometer to assess whether the attitudes of survey respondents match our theorized mechanisms. Results from survey data suggest that investments in agricultural sector capacity building will lead to reductions in emigration from developing countries; however, these findings do not indicate that rural and urban populations differ in terms of their desire to emigrate. [ABSTRACT FROM AUTHOR]

Summary This paper examines the implications of productivity improvements in agriculture, industry, and services for global poverty. We find that, in poor countries, increases in agricultural productivity generally have a larger poverty-reduction effect than increases in industry or services. This differential declines as average incomes rise, partly because agriculture becomes smaller as a share of the economy, and partly because agricultural productivity growth becomes less effective in reducing poverty. The source of the poverty-reduction benefits from agricultural productivity growth changes as innovations are more widely adopted—moving from increases in producer returns to reductions in consumer prices. [ABSTRACT FROM AUTHOR]

Stakeholders are increasingly demanding transparency on food value chain sustainability performance. Today there is no standard framework to meet this demand and support defining indicators to be used to conduct an overall sustainable performance assessment. This paper mobilizes existing frameworks and indicators to build new sustainable performance metrics for actors willing to work together for their value chain sustainability. Popular methods or tools for assessing dimensions of agrifood products or activities are selected and analyzed to determine how they could contribute to this metric. The analysis aims to distinguish the sustainable development pillars addressed (economic, environmental and/or social), the frames concerned (life cycle thinking or not; multi-actor or not), and the focus of performance measured (drivers, pressures, states, impacts, responses). This categorization is then used to develop a proposal for specifications adapted to food value chain sustainability performance assessment. The applicability of the framework is demonstrated through a case study in a pork agrifood value chain. [ABSTRACT FROM AUTHOR]

To reduce environmental pollution induced by the production of Portland cement-based building materials and eliminate the release of solid wastes from agriculture industry, this paper presents a newly-developed lightweight building material based on incorporating raw rice husk and foam bubbles in magnesium oxysulfate cement paste. Mechanical strength, bulk density, thermal conductivity and drying shrinkage of this material were carried out and phase composition, air bubble distribution and microstructure were studied on typical samples by using XRD (X-ray diffraction), FTIR (Fourier transform infrared spectroscopy), OM (opto-digitial microscope) and SEM (scanning electron microscope) facilities. The results show that raw rice husk or/and foam bubble decrease compressive strength, thermal conductivity and bulk density. Compressive strength of this composite material at 28 days decreases from 70.4 MPa to 1.1 MPa when the bulk density ranges from 1722 kg/m 3 to 450 kg/m 3 , being superior to traditional lightweight aggregate concretes. Drying shrinkage decreases with the incorporation of foam bubble and increases with the addition of raw rice husk. When the addition of raw rice husk is kept the same, there is an optimal dosage of foam bubble to obtain the lowest drying shrinkage. An excellent interfacial zone forms between raw rice husk and MOS cement paste due to numerous neat conical protrusions on the surface of rice husk particle. Therefore, this raw rice husk incorporated lightweight material has a great potential for applications to inner partition walls and thermal insulating components in buildings. [ABSTRACT FROM AUTHOR]

The increasing and often conflicting challenges that agricultural production systems face today require a more comprehensive approach to planning in the sector, integrating the economic principles of production with its social characteristics and ecological impacts. The paper presents an innovative attempt to incorporate biophysical criteria into a standard socio-economic optimisation model, illustrated through a study of the Slovenian dairy sector. The biophysical perspective on the system's functioning is determined by means of emergy analysis. This is an environmental accounting approach which reflects the cumulative environmental support needed to produce a certain output. The eco-centric perspective on the emergy approach complements the standard socioeconomic perspective of value that reflects the utility of a product (anthropocentric perspective). The model is developed based on a preceding analysis of socio-economic and emergy-based performance characteristics of different production types at the farm level that, when aggregated, constitute the sector. The multi-criteria optimisation model is supported by weighted goal programming (WGP) and aims to investigate the effects of two opposing agricultural policy paradigms on the organisation of the sector at the national level. The results show that a protectionist or eco-social focus of public interventions results in the sector's organisation with rather contrasting performance characteristics. The model outcome that represents a compromise between the two agro-political paradigms clearly suggests that incorporation of the emergy criterion into the optimisation model leads to a diverse and balanced structure and a more favourable economic and biophysical performance of the sector. Accordingly, the results confirm the complementarity of economic and emergy approaches and provide implications for a more comprehensive planning of agricultural activity. [ABSTRACT FROM AUTHOR]

This paper concerns the application of fuzzy clustering methods and fuzzy validity measures for decision support in agricultural environment. Data clustering methods, namely, K-Means, Fuzzy C-Means, Gustafson-Kessel, and Gath-Geva, are briefly reviewed and considered for analyses. The efficiency of the methods is determined by indices such as the Xie-Beni criterion, Partition Coefficient, and Partition and Dunn indices. In particular, fuzzy classifiers are developed to assist decision making regarding the control of variables such as bed moisture, temperature, and bed aeration in compost bedded pack barns. The idea is to identify interactive factors, promote cattle welfare, improve productivity indices, and increase property value. Data from 42 CBP barns in the state of Kentucky, US, were considered. Six classes related to the degree of efficiency of the composting process were identified. The GG method was the most accurate followed by the GK method. The main reason for the best results is the use of maximum-likelihood and Mahalanobis distance measures. A remark on the use of the Dunn validation index for different cluster geometries is given. Fuzzy models and linguistic information have shown to be useful to help decision making in cattle containment systems. [ABSTRACT FROM AUTHOR]

A vibrant, resilient and productive agricultural sector is fundamental to achieving the Sustainable Development Goals. Bringing about such a transformation requires optimizing a range of agronomic, environmental and socioeconomic outcomes from agricultural systems – from crop yields, to biodiversity, to human nutrition. However, these outcomes are not independent of each other – they interact in both positive and negative ways, creating the potential for synergies and trade-offs. Consequently, transforming the agricultural sector for the age of sustainable development requires tracking these interactions, assessing if objectives are being achieved and allowing for adaptive management within the diverse agricultural systems that make up global agriculture. This paper reviews the field of agricultural trade-off analysis, which has emerged to better understand these interactions – from field to farm, region to continent. Taking a “cradle-to-grave” approach, we distill agricultural trade-off analysis into four steps: 1) characterizing the decision setting and identifying the context-specific indicators needed to assess agricultural sustainability, 2) selecting the methods for generating indicator values across different scales, 3) deciding on the means of evaluating and communicating the trade-off options with stakeholders and decision-makers, and 4) improving uptake of trade-off analysis outputs by decision-makers. Given the breadth of the Sustainable Development Goals and the importance of agriculture to many of them, we assess notions of human well-being beyond income or direct health concerns (e.g. related to gender, equality, nutrition), as well as diverse environmental indicators ranging from soil health to biodiversity to climate forcing. Looking forward, areas of future work include integrating the four steps into a single modeling platform and connecting tools across scales and disciplines to facilitate trade-off analysis. Likewise, enhancing the policy relevance of agricultural trade-off analysis requires improving scientist-stakeholder engagement in the research process. Only then can this field proactively address trade-off issues that are integral to sustainably intensifying local and global agriculture – a critical step toward successfully implementing the Sustainable Development Goals. [ABSTRACT FROM AUTHOR]

Climate change (CC) directly influences agricultural sectors, presenting the need to identify both adaptation and mitigation actions that can make local farming communities and crop production more resilient. In this context, the viticultural sector is one of those most challenged by CC due to the need to combine grape quality, grapevine cultivar adaptation and therefore farmers' future incomes. Thus, understanding how suitability for viticulture is changing under CC is of primary interest in the development of adaptation strategies in traditional wine-growing regions. Considering that climate is an essential part of the terroir system, the expected variability in climate change could have a marked influence on terroir resilience with important effects on local farming communities in viticultural regions. From this perspective, the aim of this paper is to define a new dynamic viticultural zoning procedure that is able to integrate the effects of CC on grape quality responses and evaluate terroir resilience, providing a support tool for stakeholders involved in viticultural planning (winegrowers, winegrower consortiums, policy makers etc.). To achieve these aims, a Hybrid Land Evaluation System, combining qualitative (standard Land Evaluation) and quantitative (simulation model) approaches, was applied within a traditional region devoted to high quality wine production in Southern Italy (Valle Telesina, BN), for a specific grapevine cultivar (Aglianico). The work employed high resolution climate projections that were derived under two different IPCC scenarios, namely RCP 4.5 and RCP 8.5. The results obtained indicate that: (i) only 2% of the suitable area of Valle Telesina expresses the concept of terroir resilience orientated towards Aglianico ultra quality grape production; (ii) within 2010–2040, it is expected that 41% of the area suitable for Aglianico cultivation will need irrigation to achieve quality grape production; (iii) by 2100, climate change benefits for the cultivation of Aglianico will decrease, as well as the suitable areas. [ABSTRACT FROM AUTHOR]

In agricultural commodity chains, companies with sizeable market shares are stepping up sustainability commitments through so-called ‘No Deforestation, No Peat, No Exploitation’ (NDPE) policies – yet the delivery is fraught with difficulties. Drawing on theories of hybrid public-private governance this paper explores how commodity chain actors themselves view the limitations of private regulation and the prospects for more effective supply-chain governance. As a case study, we present interview data from the palm oil commodity chains linking growers in Riau Province, Sumatra, Indonesia, with retailers in Europe. The findings demonstrate awareness of shortcomings in existing arrangements and the need for a stronger presence of both the Indonesian state and European governments. We discuss potential hybrid governance measures, highlighting the need for a pluralistic strategy that mobilizes the combined positive forces of civil society, business and government(s). We argue that, to advance such an agenda, hybrid governance must be conceptualized not simply as a matter of blending (and hence reifying) pre-existing and often highly problematic private and public institutions but as a question of how all such institutions may themselves be more thoroughly democratized in the process. [ABSTRACT FROM AUTHOR]

Access to modern commercialization channels is key for smallholder farmers to move away from subsistence farming and overcome poverty. However, achieving that goal is challenging for smallholders given their lack of appropriate managerial practices, production technology and infrastructure. This paper examines the association between receiving trainings in two distinct entrepreneurial practices, one direct at the individual and farmer-association level and another indirect at the community level, and commercialization in non-local markets at the extensive and intensive margins. We exploit a panel dataset of bean producers in Nicaragua that participated in an NGO program implemented between 2007 and 2012. We find opposite results for the two market-linkage activities, especially on the intensive margin or volume of sales. While reciving direct training on entrepreneurial practices (EP) is positively associated with commercialization, training on municipality engagement (ME) activities is negatively associated. These correlation patterns are mainly observed among entrant farmers as opposed to those already participating in commercial markets prior to the program implementation. We also find varying results for ME activities by plot size and leadership position. Additional estimations show that training activities that are positively correlated with bean commercialization are not necessarily correlated with the commercialization of other crops, and vice versa. [ABSTRACT FROM AUTHOR]

Crop diseases are responsible for the major production reduction and economic losses in agricultural industry worldwide. Monitoring for health status of crops is critical to control the spread of diseases and implement effective management. This paper presents an in-field automatic wheat disease diagnosis system based on a weakly supervised deep learning framework, i.e. deep multiple instance learning, which achieves an integration of identification for wheat diseases and localization for disease areas with only image-level annotation for training images in wild conditions. Furthermore, a new in-field image dataset for wheat disease, Wheat Disease Database 2017 (WDD2017), is collected to verify the effectiveness of our system. Under two different architectures, i.e. VGG-FCN-VD16 and VGG-FCN-S, our system achieves the mean recognition accuracies of 97.95% and 95.12% respectively over 5-fold cross-validation on WDD2017, exceeding the results of 93.27% and 73.00% by two conventional CNN frameworks, i.e. VGG-CNN-VD16 and VGG-CNN-S. Experimental results demonstrate that the proposed system outperforms conventional CNN architectures on recognition accuracy under the same amount of parameters, meanwhile maintaining accurate localization for corresponding disease areas. Moreover, the proposed system has been packed into a real-time mobile app to provide support for agricultural disease diagnosis. [ABSTRACT FROM AUTHOR]

This paper reviews agro-industrial and environmental applications that are using Internet of Things (IoT). It is motivated by the need to identify application areas, trends, architectures and open challenges in these two fields. The underlying survey was developed following a systematic literature review using academic documents written in English and published in peer-reviewed venues from 2006 to 2016. Selected references were clustered into four application domains corresponding to: monitoring, control, logistics, and prediction. Implementation-specific details from each selected reference were compiled to create usage distributions of sensors, actuators, power sources, edge computing modules, communication technologies, storage solutions, and visualization strategies. Finally, the results from the review were compiled into an IoT architecture that represents a wide range of current solutions in agro-industrial and environmental fields. [ABSTRACT FROM AUTHOR]