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Unoccupied areal vehicles (UAVs or drones) are increasingly used in field research. Drones capable of routinely and consistently capturing high quality imagery of experimental fields have become relatively inexpensive. However, converting these images into scientifically useable data has become a bottleneck. A number of tools exist to support this workflow, but there is no framework for making these tools interopreable, sharable, and scalable. Here we present an initial draft of the Drone Processing Pipeline (DPP), a framework for processing agricultural research imagery that supports best practices and interoperability. DPP emphasizes open software and data that can be shared among and used in whole or part by the research community. We are building the DPP as a distributed, scalable, and flexible pipeline for converting drone imagery into orthomosaics, point clouds, and plot level statistics. Our intent is not to replace, but to integrate components from the emerging ecosystem of utilities with a focus on end-to-end automation and scalability. The initial focus of DPP is the measurements of experimental plots in field research. In the future we expect that standardization will enable new scientific discovery by facilitating collaboration and sharing of software and data. Our vision is to create a processing pipeline that is open, flexible, extensible, portable, and automated. With modern tools, deploying a pipeline on a laptop or HPC should only take a single command. Running a pipeline and publishing data should require only input data and a defined workflow.

WIREs Energy and Environment published by Wiley Periodicals LLC. As the global population increases and becomes more affluent, biomass demands for food and biomaterials will increase. Demand growth is further accelerated by the implementation of climate policies and strategies to replace fossil resources with biomass. There are, however, concerns about the size of the prospective biomass demand and the environmental and social consequences of the corresponding resource mobilization, especially concerning impacts from the associated land-use change. Strategically integrating perennials into landscapes dominated by intensive agriculture can, for example, improve biodiversity, reduce soil erosion and nutrient emissions to water, increase soil carbon, enhance pollination, and avoid or mitigate flooding events. Such “multifunctional perennial production systems” can thus contribute to improving overall land-use sustainability, while maintaining or increasing overall biomass productivity in the landscape. Seven different cases in different world regions are here reviewed to exemplify and evaluate (a) multifunctional production systems that have been established to meet emerging bioenergy demands, and (b) efforts to identify locations where the establishment of perennial crops will be particularly beneficial. An important barrier towards wider implementation of multifunctional systems is the lack of markets, or policies, compensating producers for enhanced ecosystem services and other environmental benefits. This deficiency is particularly important since prices for fossil-based fuels are low relative to bioenergy production costs. Without such compensation, multifunctional perennial production systems will be unlikely to contribute to the development of a sustainable bioeconomy. This article is categorized under: Bioenergy > Systems and Infrastructure Bioenergy > Climate and Environment Energy Policy and Planning > Climate and Environment.

We suggest that the unprecedented and unintended decrease of emissions of air pollutants during the COVID-19 lockdown in 2020 could lead to declining seasonal ozone concentrations, and positive impacts on crop yields. An initial assessment of the potential effects of COVID-19 emission reductions was made using a set of six scenarios that variously assumed annual European and global emission reductions of 30 % and 50 % for the energy, industry, road transport and international shipping sectors, and 80 % for the aviation sector. The greatest ozone reductions during the growing season reached up to 12 ppb over crop growing regions in Asia and up to 6 ppb in North America and Europe for the 50 % global reduction scenario. In Europe, ozone responses are more sensitive to emission declines in other continents, international shipping and aviation than to emissions changes within Europe. We demonstrate that for wheat the overall magnitude of ozone precursor emission changes could lead to yield improvements between 2 % and 8 %. The expected magnitude of ozone precursor emission reductions during the Northern Hemisphere growing season in 2020 presents an opportunity to test and improve crop models and experimentally-based exposure response relationships of ozone impacts on crops, under real-world conditions.

Food security is a crucial issue in the Sahel and could be endangered by climate change and demographic pressure during the 21st century. Indeed, people in rural areas mainly practice rainfed agriculture for subsistence during the monsoon rainfall season. Higher temperatures and changes in rainfall induced by global warming are threatening food production systems in this region while the population of this region is expected to increase approximately threefold until 2050 according to several demographic scenarios. However climate change is very uncertain, with monsoon rainfall that might either increase or decrease, according to different climate models and greenhouse gases emissions scenarios. Our study quantifies the impact of climate change on food security by combining climate, crops yield and demographic evolution. Using 16 global climate models, we investigate the evolution of solar radiation, temperature and precipitation under two future climatic scenarios. We simulate yield for the main crops in West-Africa: maize, sorghum and millet. Finally, we estimate in terms of production in the future and is scaled by population changes in order to assess the number of available cereal production per capita in five countries in West Africa. We found that, although uncertain, the evolution of the African monsoon is different between the countries with a rainfall increase in the Eastern Sahel for Niger and Nigeria and a decrease in the Western Sahel for Senegal under the Representative Concentration Pathway 8.5 scenario. With regard to the abundance of food for the inhabitants, all the scenarios in each country show that in 2050, local agricultural production will be below than 50 kilograms per capita, which corresponds to the basic threshold for feeding all the inhabitants of West Africa. The prospects for improving or adapting current agronomic techniques and/or expanding the cultivated areas do not allow to reach this threshold even by taking into account the [CO2] effect, which has a positive effect in drought situations. This would place additional pressures on resources in the region, with potentially strong impacts on crops import and regional migration.

The uptake of machine learning (ML) algorithms in digital soil mapping (DSM) is transforming the way soil scientists produce their maps. Machine learning is currently applied to mapping soil properties or classes much in the same way as other unrelated fields of science. Mapping of soil, however, has unique aspects which require adaptations of the ML algorithms. These features are for example, but not limited to, the inclusion of pedological knowledge into the ML algorithm, the accounting of spatial structure present in the soil data, or the desire to increase our scientific understanding of the distribution and genesis of soil from a calibrated ML model. Tackling these challenges is critical for machine learning to gain credibility and scientific consistency in soil science. In this article, we review the current applications of machine learning in digital soil mapping and suggest improvements. We found a growing interest of the use of ML in DSM. Most studies focus on obtaining accurate maps and disregard the characteristics of soil data, such as spatial autocorrelation. Only a few studies account for existing soil knowledge or quantify the uncertainty of the predicted maps. We then discuss the challenges related to the application of ML for soil mapping and offer solutions from existing studies in the natural sciences. The challenges are organized as follows: sampling, resampling, accounting for the spatial information, multivariate mapping, uncertainty analysis, validation, integration of pedological knowledge and, interpretation of the models. We conclude that for future developments, machine learning should incorporate three core elements: plausibility, interpretability, and explainability, which will trigger soil scientists to move beyond model prediction and towards explanation of soil processes.

Delivering the Sustainable Development Goals (SDGs) requires balancing demands on land between agriculture (SDG 2) and biodiversity (SDG 15). The production of vegetable oils, and in particular palm oil, illustrates these competing demands and trade-offs. Palm oil accounts for 40%1 of the current global annual demand for vegetable oil as food, animal feed, and fuel (210 million tons2 (Mt)), but planted oil palm covers less than 5-5.5%3 of total global oil crop area (ca. 425 Mha)4 , due to oil palm’s relatively high yields5. Recent oil palm expansion in forested regions of Borneo, Sumatra, and the Malay Peninsula, where >90% of global palm oil is produced, has led to substantial concern around oil palm’s role in deforestation. Oil palm expansion’s direct contribution to regional tropical deforestation varies widely, ranging from 3% in West Africa to 47% in Malaysia. Oil palm is also implicated in peatland draining and burning in Southeast Asia. Documented negative environmental impacts from such expansion include biodiversity declines, greenhouse gas emissions, and air pollution. However, oil palm generally produces more oil per area than other oil crops, is often economically viable in sites unsuitable for most other crops, and generates considerable wealth for at least some actors. Global demand for vegetable oils is projected to increase by 46% by 2050. Meeting this demand through additional expansion of oil palm versus other vegetable oil crops will lead to substantial differential effects on biodiversity, food security, climate change, land degradation, and livelihoods. Our review highlights that, although substantial gaps remain in our understanding of the relationship between the environmental, socio-cultural and economic impacts of oil palm, and the scope, stringency and effectiveness of initiatives to address these, there has been little research into the impacts and trade-offs of other vegetable oil crops. Greater research attention needs to be given to investigating the impacts of palm oil production compared to alternatives for the trade-offs to be assessed at a global scale.\ud Over the past 25 years, global oil crops have expanded rapidly, with major impacts on land use. The land used for growing oil crops grew from 170 million ha (Mha) in 1961 to 425 Mha in 2017 or ~30% of all cropland world-wide. Oil palm, soy, and rapeseed together account for >80% of all vegetable oil production with cotton, groundnuts, sunflower, olive, and coconut comprising most of the remainder (Table 1, Figure 1). These crops, including soy (125 Mha planted area) and maize (197 Mha planted area), are also used as animal feed and other products.\ud Oil palm is the most rapidly expanding oil crop. This palm originates from equatorial Africa where it has been cultivated for millennia, but it is now widely grown in Southeast Asia. Between 2008 and 2017, oil palm expanded globally at an average rate of 0.7 Mha per year, and palm oil is the leading and cheapest edible oil in much of Asia and Africa. While it has been estimated that palm oil is an ingredient in 43% of products found in British supermarkets, we lack comparable studies for the prevalence of other oils.\ud As a wild plant, the oil palm is a colonising species that establishes in open areas. Cultivated palms are commonly planted as monocultures, although the tree is also used in mixed, small-scale and agroforestry settings. To maximize photosynthetic capacity and fruit yields, oil palm requires a warm and wet climate, high solar radiation, and high humidity. It is thus most productive in the humid tropics, while other oil crops, except coconut, grow primarily in subtropical and temperate regions (Table 1). Moreover, because oil palm tolerates many soils including deep peat and sandy substrates, it is often profitable in locations where few other commodity crops are viable. The highest yields from planted oil palm have been reported in Southeast Asia5 . Yields are generally lower in Africa and the Neotropics, likely reflecting differences in climatic conditions including humidity and cloud cover, as well as management, occurrence of pests and diseases, and planting stock.\ud Palm oil is controversial due to its social and environmental impacts and opportunities. Loss of natural habitats, reduction in woody biomass, and peatland drainage that occur during site preparation are the main direct environmental impacts from oil palm development. Such conversion typically reduces biodiversity and water quality and increases greenhouse gas emissions, and, when fire is used, smoke and haze. Industrial oil palm expansion by large multi-national and national companies is also often associated with social problems, such as land grabbing and conflicts, labour exploitation, social inequity16 and declines in village-level well-being. In producer countries, oil palm is a valued crop that brings economic development to regions with few alternative agricultural development options, and generates substantial average livelihood improvements when smallholder farmers adopt oil palm. Here we review the current understanding of the environmental impacts from oil palm cultivation and assess what we know about other oil crops in comparison. Our focus is on biodiversity implications and the environmental aspects of sustainability, and we acknowledge the importance of considering these alongside socio-cultural, political, and economic outcomes.

The impact of climate change on agricultural practices is rising question marks on future food security of billions of people in tropical and sub-tropical region. Recently introduced, Climate Smart Agriculture (CSA) techniques encourages the practices of sustainable agriculture, increasing adaptive capacity and resilience to shocks at multiple levels. However, in reality, it is extremely difficult to develop a single framework for climate change resilient agricultural practices for different agrarian production landscape. Agriculture accounts for nearly 30% of Indian Gross Domestic Product (GDP) and provide livelihood of nearly two-third of the population of the country. With 60% of the agricultural land still depends on rain-fed irrigation, agrarian production economy of India has become extremely vulnerable to the monsoonal anomaly, heat stress, pest invasion and increasing rate of extreme events in upcoming decades. Due to their close relationship with environment and resources, indigenous people are considered as one of the most vulnerable community due to the changing climate. In the milieu of the climate emergency, we have selected multiple indigenous tribes from different agro-ecological zones over India in the present study to explore the adaptive potential of Indigenous Traditional Knowledge (ITK) based agricultural practices against climate change. The selected tribes are inhabitants of eastern Himalaya (Apatani), western Himalaya (Lahaulas), Eastern Ghat (Dongria-Gondh) and Western Ghat (Irular) representing rainforest, cold desert, moist upland and rain shadow landscape respectively. The effect of climate change over the respective regions has been identified using different IPCC scenario and agricultural practices resilient to climate change are quantified. Primary results indicate moderate to extreme susceptibility and preparedness of the tribes against climate change due to the exceptionally adaptive ITK based agricultural practices. A policy brief has been prepared where knowledge exchange and technology transfer among the indigenous tribes have been suggested to achieve complete climate change resiliency.

Empirical yield estimation from satellite data has long lacked suitable combinations of spatial and temporal resolutions. Consequently, the selection of metrics, i.e., temporal descriptors that predict grain yield, has likely been driven by practicality and data availability rather than by systematic targetting of critically sensitive periods as suggested by knowledge of crop physiology. The current trend towards hyper-temporal data raises two questions: How does temporality affect the accuracy of empirical models? Which metrics achieve optimal performance? We followed an in silico approach based on crop modelling which can generate any observation frequency, explore a range of growing conditions and reduce the cost of measuring yields in situ. We simulated wheat crops across Australia and regressed six types of metrics derived from the resulting time series of Leaf Area Index (LAI) against wheat yields. Empirical models using advanced LAI metrics achieved national relevance and, contrary to simple metrics, did not benefit from the addition of weather information. This suggests that they already integrate most climatic effects on yield. Simple metrics remained the best choice when LAI data are sparse. As we progress into a data-rich era, our results support a shift towards metrics that truly harness the temporal dimension of LAI data.

International audience; Crop residues are acknowledged as a key biomass resource to feed tomorrow's sustainable bioeconomy. Yet, the quantification of these residues at large geographical scales is primarily reliant upon generic statistical estimations based on empirical functions linking the residues production to the primary crop yield. These useful yet unquestioned functions are developed either using direct evidence from experimental results or literature. In the present study, analytical evidence is presented to demonstrate that these methods generate imprecise and likely inaccurate estimates of the actual biophysical crop residue potential. In this endeavor, we applied five of the most used functions to a national case study. France was selected, being the country with the largest agricultural output in Europe. Our spatially-explicit assessment of crop residues production was performed with a spatial resolution corresponding to the level of an administrative department (96 departments in total), also the finest division of the European Union's hierarchical system of nomenclature for territorial units (NUTS), and included 16 different crop residues. The theoretical potential of crop residues for France was found to vary from 987 PJ y(-1)- to 1369 PJ y(-1), using different estimation functions. The difference observed is more than the entire annual electricity consumption of Belgium, Latvia, and Estonia combined. Perturbation analyses revealed that some of the functions are overly sensitive to fluctuation in primary crop yield, while analytical techniques such as the null hypothesis statistical test indicated that the crop residues estimates stemming from all functions were all significantly different from one another.

Despite ongoing management efforts, phosphorus (P) loading from agricultural landscapes continues to impair water quality. Wastewater treatment plant research has enhanced our knowledge of microbial mechanisms influencing P cycling, especially related to microbes known as polyphosphate accumulating organisms (PAOs) that store P as polyphosphate (polyP) under aerobic conditions and release P under anaerobic conditions. However, there is limited application of PAO research to reduce agricultural P loading and improve water quality. Herein, we conducted a meta-analysis to identifying articles in Web of Science on polyP and its use by PAOs. We grouped our meta-analysis by discipline—wastewater treatment, soils and sediments (terrestrial), freshwater, marine, and agriculture—and discussed whether PAO behavior in wastewater treatment plants can be extended to natural habitats, including agricultural settings like soils, undergoing alternating anaerobic/aerobic conditions. We also synthesized knowledge gaps and research opportunities. Terrestrial, freshwater, and marine disciplines had fewer polyP and PAO articles compared to wastewater treatment and there was limited article overlap between disciplines. There is an urgent need for interdisciplinary research linking PAOs, polyP, and oxygen availability with existing knowledge of P forms and cycling mechanisms in natural and agricultural environments to improve agricultural P management strategies and achieve water quality goals.

Micromorphological report of the Hof ter Coign site (Belgium)