Early detection of pine wilt disease in Pinus tabuliformis in North China using a field portable spectrometer and UAV-based hyperspectral imagery

Background:Pine wilt disease(PWD)is a major ecological concern in China that has caused severe damage to millions of Chinese pines(Pinus tabulaeformis).To control the spread of PWD,it is necessary to develop an effective approach to detect its presence in the early stage of infection.One potential solution is the use of Unmanned Airborne Vehicle(UAV)based hyperspectral images(HIs).UAV-based HIs have high spatial and spectral resolution and can gather data rapidly,potentially enabling the effective monitoring of large forests.Despite this,few studies examine the feasibility of HI data use in assessing the stage and severity of PWD infection in Chinese pine.Method:To fill this gap,we used a Random Forest(RF)algorithm to estimate the stage of PWD infection of trees sampled using UAV-based HI data and ground-based data(data directly collected from trees in the field).We compared relative accuracy of each of these data collection methods.We built our RF model using vegetation indices(VIs),red edge parameters(REPs),moisture indices(MIs),and their combination.Results:We report several key results.For ground data,the model that combined all parameters(OA:80.17%,Kappa:0.73)performed better than VIs(OA:75.21%,Kappa:0.66),REPs(OA:79.34%,Kappa:0.67),and MIs(OA:74.38%,Kappa:0.65)in predicting the PWD stage of individual pine tree infection.REPs had the highest accuracy(OA:80.33%,Kappa:0.58)in distinguishing trees at the early stage of PWD from healthy trees.UAV-based HI data yielded similar results:the model combined VIs,REPs and MIs(OA:74.38%,Kappa:0.66)exhibited the highest accuracy in estimating the PWD stage of sampled trees,and REPs performed best in distinguishing healthy trees from trees at early stage of PWD(OA:71.67%,Kappa:0.40).Conclusion:Overall,our results confirm the validity of using HI data to identify pine trees infected with PWD in its early stage,although its accuracy must be improved before widespread use is practical.We also show UAV-based data PWD classifications are less accurate but comparable to those of ground-based data.We believe that these results can be used to improve preventative measures in the control of PWD.

Antioxidant lignans sesamin and sesamolin in sesame(Sesamum indicum L.):a comprehensive review and future prospects

Sesame(Sesamum indicum L.)is a significantly lucrative cash crop for millions of small-holder farmers.Its seeds are an important source of a highly appreciated vegetable oil globally and two clinically essential antioxidant lignans,sesamin and sesamolin.Accordingly,many countries import millions of tons of sesame seed every year.The demand for lignan-rich sesame seeds has been increasing in recent years due to the continuous discovery of several pharmacological attributes of sesamin and sesamolin.To meet this demand,the sesame breeder’s primary objective is to release sesame cultivars that are enriched in oil and lignans.Thus,it is necessary to summarize the information related to the sesamin and sesamolin contents in sesame in order to promote the joint efforts of specialized research teams on this important oilseed crop.In this article,we present the current knowledge on the sesamin and sesamolin contents in S.indicum L.with respect to the updated biosynthesis pathway,associated markers,governing loci,available variability in sesame germplasm,the in planta potential roles of these compounds in sesame,and the newly discovered pharmacological attributes.In addition,we propose and discuss some required studies that might facilitate genomics-assisted breeding of high lignan content sesame varieties.

REINTEGRATION OF CROP-LIVESTOCK SYSTEMS IN EUROPE:AN OVERVIEW

Ongoing specialization of crop and livestock systems provides socioeconomic benefits to the farmer but has led to greater externalization of environmental costs when compared to mixed farming systems.Better integration of crop and livestock systems offers great potential to rebalance the economic and environmental trade-offs in both systems.The aims of this study were to analyze changes in farm structure and review and evaluate the potential for reintegrating specialized intensive crop and livestock systems,with specific emphasis on identifying the co-benefits and barriers to reintegration.Historically,animals were essential to recycle nutrients in the farming system but this became less important with the availability of synthetic fertilisers.Although mixed farm systems can be economically attractive,benefits of scale combined with socio-economic factors have resulted in on-farm and regional specialization with negative environmental impacts.Reintegration is therefore needed to reduce nutrient surpluses at farm,regional and national levels,and to improve soil quality in intensive cropping systems.Reintegration offers practical and cost-effective options to widen crop rotations and promotes the use of organic inputs and associated benefits,reducing dependency on synthetic fertilisers,biocides and manure processing costs.Circular agriculture goes beyond manure management and requires adaptation of both food production and consumption patterns,matching local capacity to produce with food demand.Consequently,feed transport,greenhouse gas emissions,nutrient surpluses and nutrient losses to the environment can be reduced.It is concluded that reintegration of specialized farms within a region can provide benefits to farmers but may also lead to further intensification of land use.New approaches within a food system context offer alternatives for reintegration,but require strong policy incentives which show clear,tangible and lasting benefits for farmers,the environment and the wider community.

ENABLING CROP DIVERSIFICATION TO SUPPORT TRANSITIONS TOWARD MORE SUSTAINABLE EUROPEAN AGRIFOOD SYSTEMS

European cropping systems are often characterized by short rotations or even monocropping,leading to environmental issues such as soil degradation,water eutrophication,and air pollution including greenhouse gas emissions,that contribute to climate change and biodiversity loss.The use of diversification practices(i.e.,intercropping,multiple cropping including cover cropping and rotation extension),may help enhance agrobiodiversity and deliver ecosystem services while developing new value chains.Despite its benefits,crop diversification is hindered by various technical,organizational,and institutional barriers along value chains(input industries,farms,trading and processing industries,retailers,and consumers)and within sociotechnical systems(policy,research,education,regulation and advisory).Six EU-funded research projects have joined forces to boost crop diversification by creating the European Crop Diversification Cluster(CDC).This Cluster aggregates research,innovation,commercial and citizen-focused partnerships to identify and remove barriers across the agrifood system and thus enables the uptake of diversification measures by all European value-chain stakeholders.The CDC will produce a typology of barriers,develop tools to accompany actors in their transition,harmonize the use of multicriteria assessment indicators,prepare policy recommendations and pave the way for a long-term network on crop diversification.

Estimation of Soil Carbon Input in France: An Inverse Modelling Approach

Development of a quantitative understanding of soil organic carbon （SOC） dynamics is vital for management of soil to sequester carbon （C） and maintain fertility, thereby contributing to food security and climate change mitigation. There are well-established process-based models that can be used to simulate SOC stock evolution; however, there are few plant residue C input values and those that exist represent a limited range of environments. This limitation in a fundamental model component （i.e., C input） constrains the reliability of current SOC stock simulations. This study aimed to estimate crop-specific and environment-specific plant-derived soil C input values for agricultural sites in France based on data from 700 sites selected from a recently established French soil monitoring network （the RMQS database）. Measured SOC stock values from this large scale soil database were used to constrain an inverse RothC modelling approach to derive estimated C input values consistent with the stocks. This approach allowed us to estimate significant crop-specific C input values （P 〈 0.05） for 14 out of 17 crop types in the range from 1.84 =h 0.69 t C ha-1 year-1 （silage corn） to 5.15 =k 0.12 t C ha-1 year-1 （grassland/pasture）. Furthermore, the incorporation of climate variables improved the predictions. C input of 4 crop types could be predicted as a function of temperature and 8 as a function of precipitation. This study offered an approach to meet the urgent need for crop-specific and environment-specific C input values in order to improve the reliability of SOC stock prediction.