Agricultural Information Institute

Abstract Climate warming is considered to be among the most serious of anthropogenic stresses to the environment, because it not only has direct effects on biodiversity, but it also exacerbates the harmful effects of other human‐mediated threats. The associated consequences are potentially severe, particularly in terms of threats to species preservation, as well as in the preservation of an array of ecosystem services provided by biodiversity. Among the most affected groups of animals are insects—central components of many ecosystems—for which climate change has pervasive effects from individuals to communities. In this contribution to the scientists' warning series, we summarize the effect of the gradual global surface temperature increase on insects, in terms of physiology, behavior, phenology, distribution, and species interactions, as well as the effect of increased frequency and duration of extreme events such as hot and cold spells, fires, droughts, and floods on these parameters. We warn that, if no action is taken to better understand and reduce the action of climate change on insects, we will drastically reduce our ability to build a sustainable future based on healthy, functional ecosystems. We discuss perspectives on relevant ways to conserve insects in the face of climate change, and we offer several key recommendations on management approaches that can be adopted, on policies that should be pursued, and on the involvement of the general public in the protection effort.

Despite a significant growth in food production over the past half-century, one of the most important challenges facing society today is how to feed an expected population of some nine billion by the middle of the 20th century. To meet the expected demand for food without significant increases in prices, it has been estimated that we need to produce 70–100 per cent more food, in light of the growing impacts of climate change, concerns over energy security, regional dietary shifts and the Millennium Development target of halving world poverty and hunger by 2015. The goal for the agricultural sector is no longer simply to maximize productivity, but to optimize across a far more complex landscape of production, rural development, environmental, social justice and food consumption outcomes. However, there remain significant challenges to developing national and international policies that support the wide emergence of more sustainable forms of land use and efficient agricultural production. The lack of information flow between scientists, practitioners and policy makers is known to exacerbate the difficulties, despite increased emphasis upon evidence-based policy. In this paper, we seek to improve dialogue and understanding between agricultural research and policy by identifying the 100 most important questions for global agriculture. These have been compiled using a horizon-scanning approach with leading experts and representatives of major agricultural organizations worldwide. The aim is to use sound scientific evidence to inform decision making and guide policy makers in the future direction of agricultural research priorities and policy support. If addressed, we anticipate that these questions will have a significant impact on global agricultural practices worldwide, while improving the synergy between agricultural policy, practice and research. This research forms part of the UK Government's Foresight Global Food and Farming Futures project.

It is imperative to derive an appropriate cadmium (Cd) health risk toxicity threshold for paddy soils to ensure the Cd concentration of rice grains meet the food safety standard. In this study, 20 rice cultivars from the main rice producing areas in China were selected, and a pot-experiment was conducted to investigate transformation of Cd in paddy soil-rice system with 0 (CK), 0.3 mg kg−1 (T1) and 0.6 mg kg−1 (T2) Cd treatments in greenhouse. The results showed that Cd concentrations of rice grains existed significant difference (P<0.05) in 20 rice cultivars under the same Cd level in soil. The Cd concentrations of rice grains of the CK, T1 and T2 treatments were in the range of 0.143–0.202, 0.128–0.458 and 0.332–0.806 mg kg−1, respectively. Marked differences of the ratios of Cd concentration for soil to rice grain (BCFs) and transfer factors (TFs, root to grain and straw to grain) among the tested cultivars were observed in this study. The bioconcentration factors (BCFgrain) and TFs of the 20 rice cultivars were 0.300–1.112 and 0.342–0.817, respectively. The TFs of Cd from straw to grain ranged from 0.366 to 1.71, with significant differences among these 20 rice cultivars. The bioconcentration factors (BCFgrain) and TFs among the 20 rice cultivars ranged from 0.300–1.112 and 0.342–0.817, respectively. The species-sensitivity distribution (SSD) of Cd sensitivity of the rice species could be fitted well with Burr-III (R2=0.987) based on the data of BCFs. The toxicity threshold of Cd derived from SSD for the paddy soil was 0.507 mg kg−1 in the present study.

The detection, quantification, diagnosis, and identification of plant diseases is particularly crucial for precision agriculture. Recently, traditional visual assessment technology has not been able to meet the needs of precision agricultural informatization development, and hyperspectral technology, as a typical type of non-invasive technology, has received increasing attention. On the basis of simply describing the types of pathogens and host–pathogen interaction processes, this review expounds the great advantages of hyperspectral technologies in plant disease detection. Then, in the process of describing the hyperspectral disease analysis steps, the articles, algorithms, and methods from disease detection to qualitative and quantitative evaluation are mainly summarizing. Additionally, according to the discussion of the current major problems in plant disease detection with hyperspectral technologies, we propose that different pathogens’ identification, biotic and abiotic stresses discrimination, plant disease early warning, and satellite-based hyperspectral technology are the primary challenges and pave the way for a targeted response.

The accurate and reliable counting of animals in quadcopter acquired imagery is one of the most promising but challenging tasks in intelligent livestock management in the future. In this paper we demonstrate the application of the cutting-edge instance segmentation framework, Mask R-CNN, in the context of cattle counting in different situations such as extensive production pastures and also in intensive housing such as feedlots. The optimal IoU threshold (0.5) and the full-appearance detection for the algorithm in this study are verified through performance evaluation. Experimental results in this research show the framework’s potential to perform reliably in offline quadcopter vision systems with an accuracy of 94% in counting cattle on pastures and 92% in feedlots. Compared with the existing typical competing algorithms, Mask R-CNN outperforms both in the counting accuracy and average precision especially on the datasets with occlusion and overlapping. Our research shows promising steps towards the incorporation of artificial intelligence using quadcopters for enhanced management of animals.

Vision-based Continuous Sign Language Recognition (CSLR) aims to recognize unsegmented signs from image streams. Overfitting is one of the most critical problems in CSLR training, and previous works show that the iterative training scheme can partially solve this problem while also costing more training time. In this study, we revisit the iterative training scheme in recent CSLR works and realize that sufficient training of the feature extractor is critical to solving the overfitting problem. Therefore, we propose a Visual Alignment Constraint (VAC) to enhance the feature extractor with alignment supervision. Specifically, the proposed VAC comprises two auxiliary losses: one focuses on visual features only, and the other enforces prediction alignment between the feature extractor and the alignment module. Moreover, we propose two metrics to reflect overfitting by measuring the prediction inconsistency between the feature extractor and the alignment module. Experimental results on two challenging CSLR datasets show that the proposed VAC makes CSLR networks end-to-end trainable and achieves competitive performance.

This study investigates the relationship between climate variables such as rainfall amount, temperature, and carbon dioxide (CO2) emission and the triple dimension of food security (availability, accessibility, and utilization) in a panel of 25 sub-Saharan African countries from 1985 to 2018. After testing for cross-sectional dependence, unit root and cointegration, the study estimated the pool mean group (PMG) panel autoregressive distributed lag (ARDL). The empirical outcome revealed that rainfall had a significantly positive effect on food availability, accessibility, and utilization in the long run. In contrast, temperature was harmful to food availability and accessibility and had no impact on food utilization. Lastly, CO2 emission positively impacted food availability and accessibility but did not affect food utilization. The study took a step further by integrating some additional variables and performed the panel fully modified ordinary least squares (FMOLS) and dynamic ordinary least squares (DOLS) regression to ensure the robustness of the preceding PMG results. The control variables yielded meaningful results in most cases, so did the FMOLS and DOLS regression. The Granger causality test was conducted to determine the causal link, if any, among the variables. There was evidence of a short-run causal relationship between food availability and CO2 emission. Food accessibility exhibited a causal association with temperature, whereas food utilization was strongly connected with temperature. CO2 emission was linked to rainfall. Lastly, a bidirectional causal link was found between rainfall and temperature. Recommendations to the national, sub-regional, and regional policymakers are addressed and discussed.

In this work, MXene Ti3C2Tx-derived nitrogen-functionalized heterophase TiO2 homojunctions (N-MXene) were prepared via the urea-involved solvothermal treatment with varying reaction time as the sensing layer to detect trace NH3 gas at room temperature (20 °C). Compared with no signal for the pristine MXene counterpart, the 18 h-treated sensors (N-MXene-18) achieved a detection limit of 200 ppb with an inspiring response that was 7.3% better than the existing MXene-involved reports thus far. Also, decent repeatability, stability, and selectivity were demonstrated. It is noteworthy that the N-MXene-18 sensors delivered a stronger response, more sufficient recovery, and quicker response/recovery speeds under a humid environment than those under dry conditions, proving the significance of humidity. Furthermore, to suppress the effect of the fluctuation of humidity on NH3 sensing during the tests, a commercial waterproof polytetrafluoroethylene (PTFE) membrane was anchored onto the sensing layer, eventually bringing about humidity-independent features. Both nitrogen doping and TiO2 homojunctions constituted by mixed anatase and rutile phases were primarily responsible for the performance improvement with respect to pristine MXene. This work showcases the enormous potential of N-MXene materials in trace NH3 detection and offers an alternative strategy to realize both heteroatom doping and partial oxidation of MXene that is applicable in future optoelectronic devices.

Lipid degradation processes are important in microalgae because survival and growth of microalgal cells under fluctuating environmental conditions require permanent remodeling or turnover of membrane lipids as well as rapid mobilization of storage lipids. Lipid catabolism comprises two major spatially and temporarily separated steps, namely lipolysis, which releases fatty acids and head groups and is catalyzed by lipases at membranes or lipid droplets, and degradation of fatty acids to acetyl-CoA, which occurs in peroxisomes through the β-oxidation pathway in green microalgae, and can sometimes occur in mitochondria in some other algal species. Here we review the current knowledge on the enzymes and regulatory proteins involved in lipolysis and peroxisomal β-oxidation and highlight gaps in our understanding of lipid degradation pathways in microalgae. Metabolic use of acetyl-CoA products via glyoxylate cycle and gluconeogenesis is also reviewed. We then present the implication of various cellular processes such as vesicle trafficking, cell cycle and autophagy on lipid turnover. Finally, physiological roles and the manipulation of lipid catabolism for biotechnological applications in microalgae are discussed.

Continuous emission of carbon dioxide gas (CO2) poses a significant effect on ambient environment, crop production, and human health, necessitating further improvement of CO2 monitoring especially at low concentrations. To overcome the obstacles of elevated operation temperatures and faint response encountered by traditional CO2-sensitive materials such as metal oxides and perovskites, a nitrogen-doped MXene Ti3C2Tx (N-MXene)/polyethyleneimine (PEI) composite film decorated with reduced graphene oxide (rGO) nanosheets was initiatively leveraged in this work to detect 8–3000 ppm CO2 gas. Through subtle optimization in the aspects of componential constitutions, operation temperatures, PEI loading amounts, and relative humidity (RH), the ternary sensors with a PEI concentration of 0.01 mg/mL exhibited a reversible and superior performance over other counterparts under 62% RH at room temperature (20 °C). Apart from the inspiring detection limit of 8 ppm, favorable selectivity, repeatability, and long-term stability were demonstrated as well. During the humid CO2 sensing of the composites, few rGO nanosheets acted as an excellent conduction platform to transfer and collect charge carriers. Layered N-MXene offered more active sites for coadsorption of both CO2 and water, thereby facilitating the water-involving reactions. Rich amino groups of the PEI polymer were beneficial to bind CO2 molecules and thus induce appreciable density variation of charge carriers via proton-conduction behavior. This work initiatively offers an alternative ion-conduction strategy to detect ppm-level CO2 gas by harnessing rGO/N-MXene/PEI composites under a humid atmosphere at room temperature, simultaneously broadening the discrimination range of MXene-related gas sensing.

Abstract Advances in sensor miniaturization are increasing the global popularity of unmanned aerial vehicle (UAV)-based remote sensing applications in many domains of agriculture. Fruit orchards (the source of the fruit industry chain) require site-specific or even individual-tree-specific management throughout the growing season—from flowering, fruitlet development, ripening, and harvest—to tree dormancy. The recent increase in research on deploying UAV in orchard management has yielded new insights but challenges relating to determining the optimal approach (e.g., image-processing methods) are hampering widespread adoption, largely because there is no standard workflow for the application of UAVs in orchard management. This paper provides a comprehensive literature review focused on UAV-based orchard management: the survey includes achievements to date and shortcomings to be addressed. Sensing system architecture focusing on UAVs and sensors is summarized. Then up-to-date applications supported by UAVs in orchard management are described, focusing on the diversity of data-processing techniques, including monitoring efficiency and accuracy. With the goal of identifying the gaps and examining the opportunities for UAV-based orchard management, this study also discusses the performance of emerging technologies and compare similar research providing technical and comprehensive support for the further exploitation of UAVs and a revolution in orchard management.

In this work, we report on UV illumination-enhanced room-temperature trace NH3 detection based on ternary composites of reduced graphene oxide nanosheets (rGO), titanium dioxide nanoparticles (TiO2), and Au nanoparticles as the sensing layer, which is the first reported so far. The effect of the UV state as well as componential combination and content on the sensing behavior disclosed that rGO nanosheets served not only as a template to attach TiO2 and Au but also as an effective electron collector and transporter, TiO2 nanoparticles acted as a dual UV and NH3 sensitive material, and Au nanoparticles could increase the sorption sites and promote charge separation of photoinduced electron–hole pairs. The as-prepared rGO/TiO2/Au sensors were endowed with a sensing response of 8.9% toward 2 ppm of NH3, a sensitivity of 1.43 × 10–2/ppm within the investigated range, nice selectivity, robust operation repeatability, and stability, which was fairly competitive in comparison with previous work. Meanwhile, the experimental results provided clear evidence of inspiring UV-enhanced gas detection catering for the future demand of low power-consumption and high sensitivity.

Climate change severely impacts agricultural production, which jeopardizes food security. China is the second largest maize producer in the world and also the largest consumer of maize. Analyzing the impact of climate change on maize yields can provide effective guidance to national and international economics and politics. Panel models are unable to determine the group-wise heteroscedasticity, cross-sectional correlation and autocorrelation of datasets, therefore we adopted the feasible generalized least square (FGLS) model to evaluate the impact of climate change on maize yields in China from 1979–2016 and got the following results: (1) During the 1979–2016 period, increases in temperature negatively impacted the maize yield of China. For every 1°C increase in temperature, the maize yield was reduced by 5.19 kg 667 m–2 (1.7%). Precipitation increased only marginally during this time, and therefore its impact on the maize yield was negligible. For every 1 mm increase in precipitation, the maize yield increased by an insignificant amount of 0.043 kg 667 m–2 (0.014%). (2) The impacts of climate change on maize yield differ spatially, with more significant impacts experienced in southern China. In this region, a 1°C increase in temperature resulted in a 7.49 kg 667 m–2 decrease in the maize yield, while the impact of temperature on the maize yield in northern China was insignificant. For every 1 mm increase in precipitation, the maize yield increased by 0.013 kg 667 m–2 in southern China and 0.066 kg 667 m–2 in northern China. (3) The resilience of the maize crop to climate change is strong. The marginal effect of temperature in both southern and northern China during the 1990–2016 period was smaller than that for the 1979–2016 period.

Point clouds contain rich spatial information, which provides complementary cues for gesture recognition. In this paper, we formulate gesture recognition as an irregular sequence recognition problem and aim to capture long-term spatial correlations across point cloud sequences. A novel and effective PointLSTM is proposed to propagate information from past to future while preserving the spatial structure. The proposed PointLSTM combines state information from neighboring points in the past with current features to update the current states by a weight-shared LSTM layer. This method can be integrated into many other sequence learning approaches. In the task of gesture recognition, the proposed PointLSTM achieves state-of-the-art results on two challenging datasets (NVGesture and SHREC'17) and outperforms previous skeleton-based methods. To show its advantages in generalization, we evaluate our method on MSR Action3D dataset, and it produces competitive results with previous skeleton-based methods.

Cattle identification is crucial to be registered for breeding association, food quality tracing, disease prevention and control and fake insurance claims. Traditional non-biometrics methods for cattle identification is not really satisfactory in providing reliability due to theft, fraud, and duplication. In this study, a computer vision technique was proposed to facilitate precision animal management and improve livestock welfare. This paper presents a novel face identification framework by integrating light-weight RetinaFace-mobilenet with Additive Angular Margin Loss (ArcFace), namely CattleFaceNet. RetinaFace-mobilenet is designed for face detection and location, and ArcFace is adopted to strengthen the within-class compactness and also between-class discrepancy during training. Experiments on real-word scenarios dataset prove that RetinaFace-mobilenet achieves superior detection performance and significantly accelerates the computation time against RetinaNet. Three loss functions utilized in human face recognition combined with RetinaFace-mobilenet are compared and results indict that the proposed CattleFaceNet outperforms others with identification accuracy of 91.3% and processing time of 24 frames per second (FPS). This research work demonstrates the potential candidate of CattleFaceNet for livestock identification in real time in practical production scenarios.

The phenylpropanoid pathway remains a key target for most climate-resilient crop development, owing to it being a precursor to over 8000 metabolites, including flavonoids and lignin compounds, including their derivatives. These metabolites are involved in biotic and abiotic stress tolerance, inviting several studies into their roles in plant defense, drought, temperature, UV, and nutrient stress tolerance. Literature is currently inundated with cutting-edge reports on the phenylpropanoid pathways and their functions. Here, we provide a comprehensive update on the biosynthesis of phenylpropanoids, mainly lignin and flavonoids, their roles in biotic and abiotic interaction, and transcending topics, including pest and diseases, drought, temperature, and UV stress tolerance. We further reviewed the post-transcriptional, post-translational, and epigenetic modifications regulating phenylpropanoid metabolism and highlighted their applications and optimization strategies for large-scale production. This review provides an all-inclusive update on recent reports on the metabolism of phenylpropanoids in plants.

Potential of seed priming treatments in improving the performance of early planted maize was evaluated against timely planting. Seeds of maize hybrid FH-810 were soaked in water (hydropriming), CaCl2 (2.2%, osmopriming), moringa leaf extracts (MLE 3.3%, osmopriming) and salicylic acid (SA, 50 mg L−1, hormonal priming) each for 18 h. Untreated and hydroprimed seeds were taken as control. Seeds primed with SA took less time in emergence and had high vigor in early planted maize. Amongst treatments, hormonal priming, reduced the electrical conductivity, increased the leaf relative and chlorophyll contents followed by osmopriming with CaCl2 at seedling stage. Likewise, plant height, grain rows and 1 000-grain weight, grain and biological yield and harvest index were also improved by seed priming; however hormonal priming and osmopriming with MLE were more effective in this regard. Improved yield performance by hormonal priming or osmopriming with MLE in early planting primarily owed to increased leaf area index, crop growth and net assimilation rates, and maintenance of green leaf area at maturity. In conclusion, osmopriming with MLE and hormonal priming with SA were the most economical treatments in improving productivity of early planted spring maize through stimulation of early seedling growth at low temperature.

Quadcopters equipped with machine learning vision systems are bound to become an essential technique for precision agriculture applications in pastures in the near future. This paper presents a low-cost approach for livestock counting jointly with classification and semantic segmentation which provide the potential of biometrics and welfare monitoring in animals in real time. The method used in the paper adopts the state-of-the-art deep-learning technique known as Mask R-CNN for feature extraction and training in the images captured by quadcopters. Key parameters such as IoU (Intersection over Union) threshold, the quantity of the training data and the effect the proposed system performs on various densities have been evaluated to optimize the model. A real pasture surveillance dataset is used to evaluate the proposed method and experimental results show that our proposed system can accurately classify the livestock with an accuracy of 96% and estimate the number of cattle and sheep to within 92% of the visual ground truth, presenting competitive advantages of the approach feasible for monitoring the livestock.

OBJECTIVE: Microbial exposure is critical to neonatal and infant development, growth and immunity. However, whether a microbiome is present in the fetal gut prior to birth remains debated. In this study, lambs delivered by aseptic hysterectomy at full term were used as an animal model to investigate the presence of a microbiome in the prenatal gut using a multiomics approach. DESIGN: Lambs were euthanised immediately after aseptic caesarean section and their cecal content and umbilical cord blood samples were aseptically acquired. Cecal content samples were assessed using metagenomic and metatranscriptomic sequencing to characterise any existing microbiome. Both sample types were analysed using metabolomics in order to detect microbial metabolites. RESULTS: was the most abundant species in the prenatal fetal gut. We also detected multiple microbial metabolites including short chain fatty acids, deoxynojirimycin, mitomycin and tobramycin, further indicating the presence of metabolically active microbiota. Additionally, bacteriophage phiX174 and Orf virus, as well as antibiotic resistance genes, were detected in the fetal gut, suggesting that bacteriophage, viruses and bacteria carrying antibiotic resistance genes can be transmitted from the mother to the fetus during the gestation period. CONCLUSIONS: This study provides strong evidence that the prenatal gut harbours a microbiome and that microbial colonisation of the fetal gut commences in utero.

Flexible chemical sensors usually require transfer of prepared layers or whole device onto special flexible substrates and further attachment to target objects, limiting the practical applications. Herein, a sprayed gas sensor array utilizing silver nanoparticles (AgNPs)-all-carbon hybrid nanostructures is introduced to enable direct device preparation on various target objects. The fully flexible device is formed using metallic single-walled carbon nanotubes as conductive electrodes and AgNPs-decorated reduced graphene oxide as sensing layers. The sensor presents sensitive response (Ra/Rg) of 6.0–20 ppm NO2, great mechanical robustness (3000 bending cycles), and obvious sensing ability as low as 0.2 ppm NO2 at room temperature. The sensitivity is about 3.3 and 13 times as that of the sample based on metal electrodes and the sample without AgNP decoration. The fabrication method demonstrates good scalability and suitability on the planar and nonplanar supports. The devices attached on a lab coat or the human body perform stable performance, indicating practicability in wearable and portable fields. The flexible and scalable sensor provides a new choice for real-time monitoring of toxic gases in personal mobile electronics and human–machine interactions.