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Plants are affected by complex genome×environment×management interactions which determine phenotypic plasticity as a result of the variability of genetic components. Whereas great advances have been made in the cost-efficient and high-throughput analyses of genetic information and non-invasive phenotyping, the large-scale analyses of the underlying physiological mechanisms lag behind. The external phenotype is determined by the sum of the complex interactions of metabolic pathways and intracellular regulatory networks that is reflected in an internal, physiological, and biochemical phenotype. These various scales of dynamic physiological responses need to be considered, and genotyping and external phenotyping should be linked to the physiology at the cellular and tissue level. A high-dimensional physiological phenotyping across scales is needed that integrates the precise characterization of the internal phenotype into high-throughput phenotyping of whole plants and canopies. By this means, complex traits can be broken down into individual components of physiological traits. Since the higher resolution of physiological phenotyping by ‘wet chemistry’ is inherently limited in throughput, high-throughput non-invasive phenotyping needs to be validated and verified across scales to be used as proxy for the underlying processes. Armed with this interdisciplinary and multidimensional phenomics approach, plant physiology, non-invasive phenotyping, and functional genomics will complement each other, ultimately enabling the in silico assessment of responses under defined environments with advanced crop models. This will allow generation of robust physiological predictors also for complex traits to bridge the knowledge gap between genotype and phenotype for applications in breeding, precision farming, and basic research.

Weed management is a critically important activity on both agricultural and non-agricultural lands, but it is faced with a daunting set of challenges: environmental damage caused by control practices, weed resistance to herbicides, accelerated rates of weed dispersal through global trade, and greater weed impacts due to changes in climate and land use. Broad-scale use of new approaches is needed if weed management is to be successful in the coming era. We examine three approaches likely to prove useful for addressing current and future challenges from weeds: diversifying weed management strategies with multiple complementary tactics, developing crop genotypes for enhanced weed suppression, and tailoring management strategies to better accommodate variability in weed spatial distributions. In all three cases, proof-of-concept has long been demonstrated and considerable scientific innovations have been made, but uptake by farmers and land managers has been extremely limited. Impediments to employing these and other ecologically based approaches include inadequate or inappropriate government policy instruments, a lack of market mechanisms, and a paucity of social infrastructure with which to influence learning, decision-making, and actions by farmers and land managers. We offer examples of how these impediments are being addressed in different parts of the world, but note that there is no clear formula for determining which sets of policies, market mechanisms, and educational activities will be effective in various locations. Implementing new approaches for weed management will require multidisciplinary teams comprised of scientists, engineers, economists, sociologists, educators, farmers, land managers, industry personnel, policy makers, and others willing to focus on weeds within whole farming systems and land management units.

The knowledge of interactions among crop genotypes, soil types, and crop management is essential for precision agriculture. This paper explores these interactions through the analysis of 27 years of winter wheat trials, with 276 unique varieties tested across seven distinct soil types and more than 8000 plots. The study investigates how different winter wheat crop varieties respond to varying soil types and preceding crops. The findings revealed a significant interaction between variety, soil type, and preceding crop. With only a few exceptions, the highest-yielding varieties were predominantly the most recently developed. The ranking of the varieties exhibited inconsistency across the various soil types, implying that a variety yields differently when cultivated in different soil types. Furthermore, the influence of preceding crops on yield varied with soil type. This suggests that taking field-specific soil variation and the preceding crop into account during variety selection may improve the yield potential. Furthermore, the study highlights consistent yield increases due to advancements in breeding programs, with yearly increases ranging from 0.05 to 0.1 t/ha per year across all soil types. Integration of insights from genetics, soil attributes, and management practices demonstrates how farmers can increase productivity.

Phenotyping in field experiments is challenging due to interactions between plants and effects from biotic and abiotic factors which increase complexity in plant development. In such environments, visual or destructive measurements are considered the limiting factor and novel approaches are necessary. Remote multispectral imaging is a powerful method that has shown significant potential to estimate crop physiology. However, precise measurements of phenotypic differences between crop varieties in field experiments require exclusion of the disturbances caused by wind and varying sunlight. A mobile and closed multispectral imaging system was developed to study canopies in field experiments. This system shuts out wind and sunlight to ensure the highest possible precision and accuracy. Multispectral images were acquired in an experiment with four different wheat varieties, two different nitrogen levels, replicated on two different soil types at four different dates from 15 May (BBCH 13) to 18 June (BBCH 41 to 57). The images were analyzed and derived vegetation coverage and Normalized Difference Vegetation index (NDVI) were used to assess varietal differences. The results showed potentials for differentiating between the varieties using both vegetation coverage and NDVI, especially at the early growth stages. The perspectives of high-precision and high-throughput imaging for field phenotyping are discussed including the potentials of measuring varietal differences via spectral imaging in comparison to other simpler technologies such as spectral reflectance and RGB imaging.

There is an urgent need among plant breeders for a deeper understanding of the links between wheat genotypes and their ability to utilize light for biomass production and their efficiency at converting the biomass into grain yield. This field trail was conducted to investigate the variations in radiation use efficiency (RUE) and harvest index (HI) of four spring wheat cultivars grown on two soil types with two nitrogen (N) fertilization levels. Grain yield (GY) was significantly higher with 200 kg N ha−1 than 100 kg N ha−1 and on clay soil than on sandy soil, and a similar trend was observed for shoot dry matter (DM) at maturity. RUE and HI was neither affected by cultivar nor N-fertilization, but was affected by soil type, with a significantly higher RUE and HI on clay than on sandy soil. The differences of water holding capacity between the two soil types was suggested to be a major factor influencing RUE and HI as exemplified by the principal component analysis. Thus, to achieve a high RUE and/or HI, sustaining a good soil water status during the critical growth stages of wheat crops is essential, especially on sandy soils with a low water holding capacity.

Food production per unit land area needs to be increased, thus cropping systems need to use nutrients, water and solar radiation at as close to maximal efficiencies as possible. We deconstruct these efficiencies into their components to define a theoretical crop ideosystem, in which all resource use efficiencies are maximised. This defines an upper biological limit to food production. We then quantify the difference between maximum use efficiencies and those observed in three agronomic systems (maize, cocksfoot, sugarcane) and identify how, in actual farm systems, efficiencies can be raised to raise food production. We find that crop nutrient use efficiency can be limited by low water availability; thus adding nutrients would not raise production but adding water would. The converse situation of water use efficiency being affected by nutrition is not as evident. Ideosystem thinking can be used to define small- and large-scale agronomic systems that optimize water and nutrient use to maximise food production. Highlights - Novel ideosystem method of analysing processes of food production, focussing on resource use efficiencies. - Interactions between resource use efficiencies are asymmetrical. - The ideosystem concept portrays how far a production system approaches maximum efficiency.

The family of statistical models known as Rasch models started with a simple model for responses to questions in educational tests presented together with a number of related models that the Danish mathematician Georg Rasch referred to as models for measurement.

Mapping the within-field variability of crop status is of great importance in precision agriculture, which seeks to balance agronomic inputs with spatial crop demands. Satellite imagery and the delineation of management zones based on remote sensing plays a key role. However, satellite imagery is dependent on a cloud-free view, which is especially challenging in temperate regions such as Northern Europe. This disadvantage can be overcome with unmanned aerial vehicles (UAV), which provide an alternative to satellites. An investigation was conducted to establish whether UAV imagery can generate similar crop heterogeneity maps to satellites (Sentinel 2) and the extent to which crop heterogeneity and management zones can be reproduced by repeated data collection within short time intervals. Three winter wheat fields were monitored during the growing season. Two vegetation indices (NDVI and MSAVI2) based on red and near-infrared (NIR) reflectance were calculated to delineate fields into five management zones based on NDVI raster maps using quintiles. The Pearson correlation coefficient, the Nash–Sutcliffe agreement coefficient and the smallest real difference coefficient (SRD), also called the reproducibility coefficient were used to evaluate the reproducibility. NDVI and MSAVI2 gave similar results, but NDVI was a slightly better descriptor of crop heterogeneity after canopy closure and NDVI was used for the remainder of the study. The results showed that substitution of satellite data with UAV data resulted in an average reclassification of 10 m by 10 m management zones corresponding to 58% of the total field area. Reclassification means that management pixels were classified differently according to origin of images. Repeated satellite and UAV imagery resulted in 39% and 47% reclassification, respectively. The results showed that the reproduction of remote sensing data with different sensor systems added more measurement error to measurements than was the case with repeated measurements using the same sensor systems. In this study, SRD averaged 2.5 management zones, which means that differences up to 2.5 management zones were within the measurement error. This paper discusses the practical aspects of these findings and clarifies that the reclassification of management zones is depending on the heterogeneity of the studied fields. Therefore, the achieved results may not be generalized but the presented methodology can be used in future studies.

On/off patch spraying based on weed maps is used in site-specific weed management. Two prerequisites for realising patch spraying are accurate weed detection and targeting of the herbicides on weed patches. There is plenty in the literature about weed detection, but little attention has been paid to the spatial accuracy of herbicide application. This study was conducted in order to assess the extent to which patch-sprayed herbicides are targeted precisely according to pre-loaded prescription maps and to evaluate a new spatial assessment method. The new method consisted of spraying with red Ponceau 4R dye, unmanned aerial vehicle (UAV) imagery and spatial image analysis based on a geographic information system (GIS). The sprayed dye was clearly visible in aereal images and the locations of the sprayed areas were compared with the locations given by the prescription maps. Four different commercial sprayers with boom section width in the range of 0.5 to 3 m and driving speed in the range of 2.5 to 8 km h−1 were used in ten experiments. All the experiments were carried out in autumn in stubble fields. The results showed that the new method was fast and reliable. The incorrectly sprayed area outside targeted areas on prescription maps averaged 81% for three different sprayers with 3 m boom sections, and 5% for a sprayer with 0.5 m boom sections (individual spray nozzle control). The target areas not sprayed within the planned weed patch areas averaged 6% of the pre-defined patch area for sprayers with 3 m boom sections, and 14% for the sprayer with 0.5 m boom sections. This study revealed that the sprayer with 0.5 m boom sections had a controller that was not quick enough at opening and closing spray nozzles at normal driving speeds. Log files from the sprayer console overestimated the sprayed area by 24% and were less accurate than the spatial analysis of the sprayed areas.