Abstract Intensive agriculture in Germany is not only highly productive but has also led to detrimental effects in the environment. Crop diversification together with new field arrangements considering soil heterogeneities can be an alternative to improve resource use efficiency (RUE), ecosystem services (ESS), and biodiversity. Agroecosystem models are tools that help us to understand and design diversified new field arrangements. The main goal of this study was to review the extent to which agroecosystem models have been used for crop diversification design at field and landscape scale by considering soil heterogeneities and to understand the model requirements for this purpose. We found several agroecosystem models available for simulating spatiotemporal crop diversification at the field scale. For spatial crop diversification, simplified modelling approaches consider crop interactions for light, water, and nutrients, but they offer restricted crop combinations. For temporal crop diversification, agroecosystem models include the major crops (e.g., cereals, legumes, and tuber crops). However, crop parameterization is limited for marginal crops and soil carbon and nitrogen (N). At the landscape scale, decision-making frameworks are commonly used to design diversified cropping systems. Within-field soil heterogeneities are rarely considered in field or landscape design studies. Combining static frameworks with dynamic agroecosystems models can be useful for the design and evaluation of trade-offs for ESS delivery and biodiversity. To enhance modeling capabilities to simulate diversified cropping systems in new field arrangements, it will be necessary to improve the representation of crop interactions, the inclusion of more crop species options, soil legacy effects, and biodiversity estimations. Newly diversified field arrangement design also requires higher data resolution, which can be generated via remote sensing and field sensors. We propose the implementation of a framework that combines static approaches and process-based models for new optimized field arrangement design and propose respective experiments for testing the combined framework. 

https://link.springer.com/content/pdf/10.1007/s13593-022-00805-4.pdf

Model-based design of crop diversification through new field arrangements in spatially heterogeneous landscapes. A review

Innovations in digital technologies, especially in artificial intelligence (AI), promise substantial benefits to the agricultural sector. Agriculture is increasingly expected to ensure food security and food safety while at the same time considering the environmental aspects. AI in the agricultural sector offers the potential to feed a continuously growing global population and still contribute to achieving the UN’s Sustainable Development Goals (SDGs). Despite its promises, the use of AI in agriculture is still limited. We argue that the slow uptake is due to the diverse ways in which AI impacts the agri-food industry, due to the diversity of foods, supply chains, climates, and land in the agricultural sector. We propose that this is also exacerbated by ethical concerns arising from AI use, the varying degrees of technological development and skills, and the economic impacts of agricultural AI. A literature review of multiple disciplines in agricultural AI (economic, environmental, social, ethical, and technological) and a focus group of experts. AI-powered systems in agriculture raise various sets of concerns in multiple disciplines that need to be aligned to provide sustainable AI solutions for the agriculture domain. Our research proposes that it is important to adopt an interdisciplinary approach when developing AI in agriculture. AI in agriculture should be developed by interdisciplinary collaboration because it has a greater chance to be robust, economically-valuable and socially desirable, which may lead to greater acceptance and trust among farmers when using it. 

An interdisciplinary approach to artificial intelligence in agriculture

An improved semantic segmentation method based on object contextual representations network (OCRNet) is proposed to accurately identify zucchinis intercropped with sunflowers from unmanned aerial vehicle (UAV) visible images taken over Hetao Irrigation District, Inner Mongolia, China. The proposed method improves on the performance of OCRNet in two respects. First, based on the object region context extraction structure of the OCRNet, a branch that uses the channel attention module was added in parallel to rationally use channel feature maps with different weights and reduce the noise of invalid channel features. Secondly, Lovasz-Softmax loss was introduced to improve the accuracy of the object region representation in the OCRNet and optimize the final segmentation result at the object level. We compared the proposed method with extant advanced semantic segmentation methods (PSPNet, DeepLabV3+, DNLNet, and OCRNet) in two test areas to test its effectiveness. The results showed that the proposed method achieved the best semantic segmentation effect in the two test areas. More specifically, our method performed better in processing image details, segmenting field edges, and identifying intercropping fields. The proposed method has significant advantages for crop classification and intercropping recognition based on UAV visible images, and these advantages are more substantive in object-level evaluation metrics (mIoU and intercropping IoU).

https://www.mdpi.com/2072-4292/13/14/2706/pdf

Recognizing Zucchinis Intercropped with Sunflowers in UAV Visible Images Using an Improved Method Based on OCRNet

We provide an overview of the Special Issue on current advances, challenges, and opportunities for AI technologies in agriculture. We illustrate the potential of AI using four major components of the food system: production, distribution, consumption, and uncertainty. We recognize that the transformation of agriculture will require new tools to more precisely manage fields to increase production while minimizing the environmental risk to water and air quality. Combining AI with other technologies will be needed to provide effective production management strategies for a given combination of soil, climate, pest complexes, and vegetation. New methods will be needed to determine production limitations, and effective management options. The agricultural enterprise is prime for the use of AI and other technologies if they can be adapted for the unique characteristics of agroecosystems, including variability and directional changes in climate and other global change drivers as well as novel management and policy decisions, and economic market volatility.

Harnessing AI to Transform Agriculture and Inform Agricultural Research

Natural diversity in fatty acids profiles and antioxidant properties of sumac fruits (Rhus coriaria L.): Selection of preferable populations for food industries

Genome-wide association study identifies acyl-lipid metabolism candidate genes involved in the genetic control of natural variation for seed fatty acid traits in Brassica napus L.

Introduction: Wrinkle ridges are compressional tectonic features that are produced through a combination of folding and faulting in the subsurface [1]. These features exhibit variability in ridge height, length, and morphology. On Mars, wrinkle ridges are common throughout the volcanic surfaces, particularly within the tectonically active region of Tharsis. They are observed to be tens to hundreds of kilometers long, with typical widths of ten kilometers [2]. There are at least four distinct morphological types of ridges: symmetric, asymmetric ramp, arch, and double ridges [3] with arches being less common. Many wrinkle ridges also demonstrate an offset between the elevation of the surrounding surface beyond the forelimb and backlimb, interpreted as evidence for a lithosphere-scale thrust fault [2]. Alternatively, other researchers interpret wrinkle ridges as thin-skinned structures confined to the volcanic plains [1]. However, the depths to which the faults propagate, and the dips associated with these faults remain unknown. Previous studies have applied elastic dislocation models to constrain the depth and geometry of faults in the subsurface [1,3-4]. The aim of this study is to first examine the variability of wrinkle ridge morphology within individual ridges, and then apply a simpler geometric approach to constrain the geometry of faults. We find that ridge morphology can vary significantly within a single ridge. Most ridges demonstrate a topographic step and have profiles consistent with a steeping of the fault dip at shallow depths. Methods: Using Mars Orbiter Laser Altimeter (MOLA) topography data, the morphology of a set of ridges in Solis and Lunae Plana was analyzed through hundreds of profiles produced perpendicular to the ridges. The profiles for a given ridge were then grouped into clusters of similar morphology using a k-means classification algorithm, which iteratively adjusts the clusters based on the RMS difference between each profile and the average profile of the clusters. By assigning three or four distinct groupings, the variability in the morphology of individual ridges becomes clear. The average profiles of the clusters can be classified according to common morphological types as symmetric, asymmetric ramp, and double ridge (Fig. 1). Double ridges are classified as having asymmetric forelimb and backlimb, with a secondary ridge (also described as a broad arch and narrow ridge, or primary and secondary antiform) [1,3-5]. Fig. 1. Three common classifications of wrinkle ridge morphology. Symmetric (a), asymmetric ramp (b), double ridge (c).

Morphology and subsurface structure of wrinkle ridges on mars

Remote sensing has proven to provide agriculture with many different assessments for crop vigor and productivity. The continual evolution of remote sensing instrumentation and platforms has provided new opportunities to use these tools in the assessment of agricultural systems. The application of remote sensing to quantify the spatial variation in production fields across the Midwest over multiple years has revealed there are three stability zones: the high yielding stable zone, the low yielding stable zone, and the unstable zone. These are derived using a combination of thermal images to detect areas of water stress and the normalized difference vegetative index to assess crop vigor and efficiency of light capture. Development of tools using remote sensing coupled with artificial intelligence and machine learning can transform agriculture through the ability to identify variable areas within fields but also determine the potential adaptive strategies to increase the profitability for each field while reducing the environmental impact through more efficient use of nutrients and pesticides. Development of new tools using remote sensing fulfills the vision of integrating many sources of information into decision making at the field and farm scale.

Remote Sensing: Advancing the Science and the Applications to Transform Agriculture

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Genome-wide association study identifies acyl-lipid metabolism candidate genes involved in the genetic control of natural variation for seed fatty acid traits in Brassica napus L.

Morphology and subsurface structure of wrinkle ridges on mars

Remote Sensing: Advancing the Science and the Applications to Transform Agriculture