Crop Patches Research Articles

An Integrated Approach to Unravelling Smallholder Yield Levels: The Case of Small Family Farms, Eastern Region, Ghana

Yield levels and the factors determining crop yields is an important strand of research on rainfed family farms. This is particularly true for Sub-Saharan Africa (SSA), which reports some of the lowest crop yields. This also holds for Ghana, where actual yields of maize, the most important staple crop, are currently about only a third of achievable yields. Developing a comprehensive understanding of the factors underpinning these yield levels is key to improving them. Previous research endeavours on this frontier have been incumbered by the mono-disciplinary focus and/or limitations relating to spatial scales, which do not allow the actual interactions at the farm level to be explored. Using the sustainable livelihoods framework and, to a lesser extent, the induced innovation theory as inspiring theoretical frames, the present study employs an integrated approach of multiple data sources and methods to unravel the sources of current maize yield levels on smallholder farms in two farming villages in the Eastern region of Ghana. The study relies on farm and household survey data, remotely-sensed aerial photographs of maize fields and photo-elicitation interviews (PEIs) with farmers. These data cover the 2016 major farming season that spanned the period March–August. We found that the factors that contributed to current yield levels are not consistent across yield measures and farming villages. From principal component analysis (PCA) and multiple linear regression (MLR), the timing of maize planting is the most important determinant of yield levels, explaining 25% of the variance in crop cut yields in Akatawia, and together with household income level, explaining 32% of the variance. Other statistically significant yield determinants include level of inorganic fertiliser applied, soil penetrability and phosphorus content, weed control and labour availability. However, this model only explains a third of the yields, which implies that two-thirds are explained by other factors. Our integrated approach was crucial in further shedding light on the sources of the poor yields currently achieved. The aerial photographs enabled us to demonstrate the dominance of poor crop patches on the edges and borders of maize fields, while the PEIs further improved our understanding of not just the causes of these poor patches but also the factors underpinning delayed planting despite farmers’ awareness of the ideal planting window. The present study shows that socioeconomic factors that are often not considered in crop yield analyses—land tenure and labour availability—often underpin poor crop yields in such smallholder rainfed family farms. Labour limitations, which show up strongly in both in the MLR and qualitative data analyses, for example, induces certain labour-saving technologies such as multiple uses of herbicides. Excessive herbicide use has been shown to have negative effects on maize yields.

Locomotor behaviour promotes stability of the patchy distribution of slugs in arable fields: Tracking the movement of individual Deroceras reticulatum.

The distribution of the grey field slug (Deroceras reticulatum Müller) in arable fields is characterised by patches containing higher slug densities dispersed within areas of lower densities. Behavioural responses that lead to the spatial/temporal stability of these patches are poorly understood, thus this study investigated behavioural mechanisms underpinning slug distribution using a new method for long-term tracking of individual slug movement in the field. A technique for implanting radio frequency identification (RFID) tags (each with a unique identification code) beneath the body wall of slugs was developed. Laboratory tests indicated no consistent detrimental effect on survival, feeding, egg laying or locomotor behaviour (velocity, distance travelled). Movement of individual slugs above and below the soil surface was recorded for >5 weeks (in spring and autumn) in winter wheat fields. Most (~80%) foraged within a limited area; and at the end of the observation period were located at a mean distance of 78.7 ± 33.7 cm (spring) or 101.9 ± 24.1 cm (autumn) from their release point. The maximum detected distance from the release point was 408.8 cm. The remaining slugs (~20%) moved further away and ultimately were lost. RFID tagging allowed continuous tracking of individual slugs, even below the soil surface. Localised movement of 80% of tracked slugs over 5 weeks offers a mechanism promoting stable slug patches in arable crops. Rapid dispersal of the remaining slugs facilitates exchange of individuals between patches. Precision targeting of pesticides at such stable slug patches may facilitate reduced usage. © 2020 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Patch Proposal Network for Fast Semantic Segmentation of High-Resolution Images

Despite recent progress on the segmentation of high-resolution images, there exist an unsolved problem, i.e., the trade-off among the segmentation accuracy, memory resources and inference speed. So far, GLNet is introduced for high or ultra-resolution image segmentation, which has reduced the computational memory of the segmentation network. However, it ignores the importances of different cropped patches, and treats tiled patches equally for fusion with the whole image, resulting in high computational cost. To solve this problem, we introduce a patch proposal network (PPN) in this paper, which adaptively distinguishes the critical patches from the trivial ones to fuse with the whole image for refining segmentation. PPN is a classification network which alleviates network training burden and improves segmentation accuracy. We further embed PPN in a global-local segmentation network, instructing global branch and refinement branch to work collaboratively. We implement our method on four image datasets:DeepGlobe, ISIC, CRAG and Cityscapes, the first two are ultra-resolution image datasets and the last two are high-resolution image datasets. The experimental results show that our method achieves almost the best segmentation performance compared with the state-of-the-art segmentation methods and the inference speed is 12.9 fps on DeepGlobe and 10 fps on ISIC. Moreover, we embed PPN with the general semantic segmentation network and the experimental results on Cityscapes which contains more object classes demonstrate the generalization ability on general semantic segmentation.

Viewport-Based CNN: A Multi-Task Approach for Assessing 360° Video Quality.

For 360° video, the existing visual quality assessment (VQA) approaches are designed based on either the whole frames or the cropped patches, ignoring the fact that subjects can only access viewports. When watching 360° video, subjects select viewports through head movement (HM) and then fixate on attractive regions within the viewports through eye movement (EM). Therefore, this paper proposes a two-staged multi-task approach for viewport-based VQA on 360° video. Specifically, we first establish a large-scale VQA dataset of 360° video, called VQA-ODV, which collects the subjective quality scores and the HM and EM data on 600 video sequences. By mining our dataset, we find that the subjective quality of 360° video is related to camera motion, viewport positions and saliency within viewports. Accordingly, we propose a viewport-based convolutional neural network (V-CNN) approach for VQA on 360° video, which has a novel multi-task architecture composed of a viewport proposal network (VP-net) and viewport quality network (VQ-net). The VP-net handles the auxiliary tasks of camera motion detection and viewport proposal, while the VQ-net accomplishes the auxiliary task of viewport saliency prediction and the main task of VQA. The experiments validate that our V-CNN approach significantly advances state-of-the-art VQA performance on 360° video and it is also effective in the three auxiliary tasks.

A Fast and Accurate Algorithm for Nuclei Instance Segmentation in Microscopy Images

Nuclei instance segmentation within microscopy images is a fundamental task in the pathology work-flow, based on that the meaningful nuclear features can be extracted and multiple biological related analysis can be performed. However, this task is still challenging because of the large variability among different types of nuclei. Although deep learning(DL) based methods have achieved state-of-the-art results in nuclei instance segmentation tasks, these methods are usually focus on improving the accuracy and require support of powerful computing resources. In this paper, we joint the detection and segmentation simultaneously, and propose a fast and accurate box-based nuclei instance segmentation method. Mainly, we employ a fusion module based on the feature pyramid network(FPN) to combine the complementary information of the shallow layers with deep layers for detection the nuclear location by bounding boxes. Subsequently, we crop the feature maps according to the bounding boxes and feed the cropped patches into an U-net architecture as a guide to separate clustered nuclei. The experiments show that the proposed approach outperforms prior state-of-the-art methods, not only on accuracy but also on speed. The source code will be released at: https://github.com/QUAPNH/Nucleiseg.

Hybrid Nonsmooth Barrier Functions With Applications to Provably Safe and Composable Collision Avoidance for Robotic Systems

Robots are entering an age of ubiquity, and to operate effectively, these systems must typically satisfy a series of constraints (e.g., collision avoidance, obeying speed limits, maintaining connectivity). In addition, modern applications hinge on the completion of particular tasks, such as driving to a certain location or monitoring a crop patch. The dichotomy between satisfying constraints and completing objectives creates a need for constraint-satisfaction frameworks that are composable with a pre-existing primary objective. Barrier functions have recently emerged as a practical and the composable method for constraint satisfaction, and prior results demonstrate a system of Boolean logic for nonsmooth barrier functions as well as a composable controller-synthesis framework; however, this prior work does not consider dynamically changing constraints (e.g., a robot sensing and avoiding an obstacle). Consequently, the main theoretical contribution of this letter extends nonsmooth barrier functions to time-varying barrier functions with jumps. In a practical instantiation of the theoretical main results, this letter revisits a classic problem by formulating a collision-avoidance framework and composing it with a nominal controller. Experimental results show the efficacy of this framework on a light detection and ranging (LIDAR)-equipped differential-drive robot in a real-time obstacle-avoidance scenario.

Host response of Agaricus bisporus to mushroom virus X infection

Commercial mushroom crops (Agaricus bisporus) are susceptible to a disease causedby a complex of 18 viruses known collectively as mushroom virus X (MVX). Symptoms of MVX infection vary from bare patches in crops to mushroom discolouration (browning). To understand the dynamic interaction between A. bisporus and MVX, we have studied five strains; including the globally-cultivated commercial strain, one wild strain, and one commercial-wild hybrid strain. Our transmission experiments using ‘healthy’ mycelium challenged with MVX-infected mycelium, detected MVX in the first day of hyphal anastomosis in all five strains. However, our commercial-scale crop experiment revealed varying degrees of disease tolerance in the fruiting body, the commercial strain being most susceptible and the hybrid strain most resistant to MVX. LC-MS/MS proteomics and RNA-seq analyses have elucidated key differences in response to both early and late crop inoculation of MVX. Quantitative shotgun proteomics of the susceptible commercial strain at late MVX inoculation revealed striking levels of proteins relating to mechanical membrane damage via detection of myo-inositol-associated biosynthetic proteins. Defense proteins relating to chalcone isomerase activity were also detected exclusively in the MVX-infected commercial strain. MVX-infected wild strain isolates showed significantly greater abundance of proteins related to fundamental cellular processes such as RNA polymerase activity and cell-redox homeostasis. Our findings show that although vegetative transmission of MVX is prevalent in all five strains, the fruiting body may oppose the infection in certain strains. Our findings of dynamic host response of A. bisporus to MVX, provides novel insights for this economically important, globally cultivated crop.

A knowledge-based approach for discriminating multi-crop scenarios using multi-temporal polarimetric SAR parameters

ABSTRACTIn this article, we evaluate a series of POLSAR (Polarimetric Synthetic Aperture Radar) parameters and devise a robust, multi-date, and hierarchical decision tree algorithm for crop discrimination. The study area is a farmland in North India having relatively large patches of winter crops with medium heterogeneity. Ten POLSAR parameters are evaluated for this work including polarimetric entropy (), polarimetric alpha angle (), Yamaguchi decomposition components, radar vegetation index (RVI), volume scattering index, canopy scattering index, biomass index, and pedestal height (PH). We choose polarimetric RVI, polarimetric entropy, and polarimetric alpha angle parameter, after sensitivity analysis, to be incorporated into building the multi-temporal POLSAR decision tree. The proposed algorithm accepts, as input, a precisely co-registered multi-date stack of fully POLSAR imagery. The algorithm makes use of the temporal profiles of the selected POLSAR parameters in achieving the crop discrimination. We provide a quantitative assessment of classification accuracy of various classes based on extensive ground truth data. The multi-temporal algorithm is compared with the well-established, single-date, supervised Wishart classifier. Statistically significant improvement of accuracies is observed across various classes as compared to single-date methodology. Our study suggests that whenever ground truth data are extensively available, a supervised classifier based on carefully chosen multi-temporal POLSAR parameters yield very compelling crop discrimination capabilities.

Classification of mice hepatic granuloma microscopic images based on a deep convolutional neural network

Hepatic granuloma develops in the early stage of liver cirrhosis which can seriously injury liver health. At present, the assessment of medical microscopic images is necessary for various diseases and the exploiting of artificial intelligence technology to assist pathology doctors in pre-diagnosis is the trend of future medical development. In this article, we try to classify mice liver microscopic images of normal, granuloma-fibrosis1 and granuloma-fibrosis2, using convolutional neural networks (CNNs) and two conventional machine learning methods: support vector machine (SVM) and random forest (RF). On account of the included small dataset of 30 mice liver microscopic images, the proposed work included a preprocessing stage to deal with the problem of insufficient image number, which included the cropping of the original microscopic images to small patches, and the disorderly recombination after cropping and labeling the cropped patches In addition, recognizable texture features are extracted and selected using gray the level co-occurrence matrix (GLCM), local binary pattern (LBP) and Pearson correlation coefficient (PCC), respectively. The results established a classification accuracy of 82.78% of the proposed CNN based classifiers to classify 3 types of images. In addition, the confusion matrix figures out that the accuracy of the classification results using the proposed CNNs based classifiers for the normal class, granuloma-fibrosis1, and granuloma-fibrosis2 were 92.5%, 76.67%, and 79.17%, respectively. The comparative study of the proposed CNN based classifier and the SVM and RF proved the superiority of the CNNs showing its promising performance for clinical cases.

Lost forever? Ecosystem functional changes occurring after agricultural abandonment and forest recovery in the semiarid Chaco forests

Semiarid forests are worldwide threatened by land use changes, particularly agriculture. However, in some cases, due to particular economic or social processes, agriculture ends and forests may or may not recover to their original state. Using different databases and satellite images integrated into a geographical information system, we located in the central region of the semiarid Chaco forests of Argentina adjacent land use patches of secondary forest (SF), remnant forest (RF) and crops (CP). Using a chronosequence approach, we evaluated changes in the fraction of the photosynthetic active radiation absorbed by the vegetation (FAPAR) between SF and RF and CP, using the enhanced vegetation index (EVI). We evaluated both intra and inter-annual changes in EVI mean (EVImean), EVI maximum (EVImax), EVI minimum (EVImin), and EVI relative range (EVIrr) as descriptors of FAPAR dynamics and analyzed their changes through time (2000 to 2010) and their relation to rainfall. Secondary forests showed higher seasonality and higher EVImean values than RF, but differences disappeared as time since agricultural abandonment increased, suggesting that SF recover their functioning (when compared to RF) after 10 to 15 years. Our results suggest that Chaco's SF have intermediate seasonal patterns in-between RF and CP, as expected by successional theory, and that FAPAR interception by RF appears to be dependent on previous year's precipitation. We found that, although all land uses showed similar precipitation use efficiency (PUE), SF and cropland's productivity were less stable across the years and showed faster increases or decreases compared to RF, depending on precipitation (higher precipitation marginal response- PMR). Our results suggest that at least some aspects of ecosystem functioning can be restored after agricultural abandonment. Future research that combines floristic and structural changes is necessary to fully understand secondary forests regrowth process after agricultural abandonment in the Chaco region.

Automatic texture exemplar extraction based on global and local textureness measures

Texture synthesis is widely used for modeling the appearance of virtual objects. However, traditional texture synthesis techniques emphasize creation of optimal target textures, and pay insufficient attention to choice of suitable input texture exemplars. Currently, obtaining texture exemplars from natural images is a labor intensive task for the artists, requiring careful photography and significant postprocessing. In this paper, we present an automatic texture exemplar extraction method based on global and local textureness measures. To improve the efficiency of dominant texture identification, we first perform Poisson disk sampling to randomly and uniformly crop patches from a natural image. For global textureness assessment, we use a GIST descriptor to distinguish textured patches from non-textured patches, in conjunction with SVM prediction. To identify real texture exemplars consisting solely of the dominant texture, we further measure the local textureness of a patch by extracting and matching the local structure (using binary Gabor pattern (BGP)) and dominant color features (using color histograms) between a patch and its sub-regions. Finally, we obtain optimal texture exemplars by scoring and ranking extracted patches using these global and local textureness measures. We evaluate our method on a variety of images with different kinds of textures. A convincing visual comparison with textures manually selected by an artist and a statistical study demonstrate its effectiveness.

Towards sustainable rural landscapes? a multivariate analysis of the structure of traditional tree cropping systems along a human pressure gradient in a mediterranean region

The worldwide increase of human pressure in rural areas has resulted in a progressive fragmentation of agro-forest landscapes. Relatively few studies have identified processes of landscape modification and patch fragmentation reflecting long-term human activity from those caused by recent urbanization stimulated by economic development, population growth and improved accessibility of rural areas. In rural districts dominated by high-quality crop mosaics and exposed to increasing levels of human pressure, urbanization-driven changes in the use of land have determined complex and non-linear processes of landscape fragmentation which deserve further investigation. Multidimensional procedures and data mining are appropriate to explore such transformations, basing on a comprehensive assessment of landscape metrics. The present study investigates changes in land-use and landscape structure (2000–2008) in three districts of Latium (central Italy) featuring different cropping systems, physiographic attributes and level of human pressure. A principal component analysis was run to identify a core set of metrics aimed at (i) evaluating the role of traditional cropping systems in the preservation of traditional agro-forest systems and (ii) inferring the relationship between spatial heterogeneity in rural landscapes and socio-ecological processes of change. Our results indicate that crop intensification has contributed to landscape homogeneity and simplification in all areas studied, in contrast with the effects of urban expansion resulting in a more fragmented and diversified landscape with relict tree crop patches. The findings of this study clarify the contribution of traditional tree crop systems to sustainable structures and functions of rural landscapes by preserving the place-specific eco-mosaic complexity.

Riverine Landscape Patches Influence Trophic Dynamics of Riparian Ants

AbstractFood webs in riparian corridors are increasingly viewed as embedded in complex riverine landscapes characterized by an amalgam of aquatic, semi‐aquatic, and terrestrial habitats. However, the influence of riverine landscape pattern on trophic dynamics of riparian consumers remains largely unknown. We used naturally abundant stable isotope ratios (δ15N) to compare trophic structure of ants (Formica subsericea) among riparian patch types (crop, grass/herbaceous, gravel bar, lawn, mudflat, shrub, swamp, and woody vegetation) at 12 riverine landscapes distributed along an urban‐rural landscape gradient of the Scioto River, Ohio, USA. We expected that the diet of F. subsericea, a common generalist consumer, would reflect local prey availability and thus differences in trophic dynamics among patch types. Mean ant δ15N was higher in crop patches than in any other patch type, and was lowest in grass/herbaceous, lawn, shrub, and woody vegetation patches, suggesting that patch type was associated with trophic position of F. subsericea. We also found that the range of δ15N, and thus trophic breadth, was significantly different by patch type, with woody vegetation exhibiting the greatest spread. Variability in canopy, tree and shrub cover, and the degree of urban development was positively correlated with δ15N range (R2 = 0.78), pointing to the role of habitat structure in mediating trophic breadth of riparian ants. These findings provide evidence that riverine landscape pattern can strongly influence trophic dynamics of riparian arthropods. Copyright © 2016 John Wiley & Sons, Ltd.

Aphid parasitoids respond to vegetation heterogeneity but not to fragmentation scale: An experimental field study

How animal populations respond to habitat manipulations is a central theme in ecology. In recent years, the role that vegetation heterogeneity plays in regulating arthropod populations has received particular attention in both conservation science and agricultural ecology. Numerous observational studies have demonstrated that herbivores and their natural enemies are sensitive to vegetation heterogeneity, but the individual effects of percentage land cover, degree of fragmentation and patch size remain little understood. We present here the results of a manipulative field experiment that explicitly incorporates both habitat heterogeneity and the degree to which that heterogeneity is fragmented in order to determine the effects of each factor on parasitism in an agroecosystem. We deployed combinations of broccoli (crop) and weedy vegetation (non-crop) in linear arrays that varied in their percentage devoted to crop and in the degree at which crop patches were fragmented with weeds, and recorded parasitism rates on two aphid species multiple times during two years. Parasitoids responded to the percentage of crop in plots, but not to the spatial scale at which they were fragmented. Our results suggest that vegetation heterogeneity may be more important than fragmentation scale in biological control by parasitoids.

Estimation of Trees Outside Forests using IRS High Resolution data by Object Based Image Analysis

Abstract. Assessment of Trees outside forests (TOF) is widely being recognized as a pivotal theme, in sustainable natural resource management, due to their role in offering variety of goods, such as timber, fruits and fodder as well as services like water, carbon, biodiversity. Forest Conservation efforts involving reduction of deforestation and degradation may have to increasingly rely on alternatives provided by TOF in catering to economic demands in forest edges. Spatial information systems involving imaging, analysis and monitoring to achieve objectives under protocols like REDD+, require incorporation of information content from areas under forest as well as trees outside forests, to aid holistic decisions. In this perspective, automation in retrieving information on area under trees, growing outside forests, using high resolution imaging is essential so that measuring and verification of extant carbon pools, are strengthened. Retrieval of this tree cover is demonstrated herewith, using object based image analysis in a forest edge of dry deciduous forests of Eastern Ghats, in Khammam district of Telangana state of India. IRS high resolution panchromatic 2.5 m data (Cartosat-1 Orthorectified) used in tandem with 5.8 m multispectral LISS IV data, discerns tree crowns and clusters at a detailed scale and hence semi-automated approach is attempted to classify TOF from a pair of image from relatively crop and cloud free season. Object based image analysis(OBIA) approach as implemented in commercial suite of e-Cognition (Ver 8.9) consists of segmentation at user defined scale followed by application of wide range of spectral, textural and object geometry based parameters for classification. Software offers innovative blend of raster and vector features that can be juxtaposed flexibly, across scales horizontally or vertically. Segmentation was carried out at multiple scales to discern first the major land covers, such as forest, water, agriculture followed by that at a finer scale, within cultivated landscape. Latter scale aimed to segregate TOF in configurations such as individual or scattered crowns, linear formations and patch TOF. As per the adopted norms in India for defining tree cover, units up to 1 ha area were considered as candidate TOF. Classification of fine scale (at 10) segments was accomplished using size, shape and texture. A customised parameter involving ratio of area of segment to its main skeleton length discerned linear formations consistently. Texture of Cartosat-1 2.5 m data was also used segregate tree cover from smoother crop patches in patch TOF category. In view of the specificity of the landscape character, continuum of cultivated area (b) and pockets of cultivation within forest (c) as well as the entire study area (a) were considered as three envelopes for evaluating the accuracy of the method. Accuracies not less than 75.1 per cent were reported in all the envelopes with a kappa accuracy of not less than 0.58. Overall accuracy of entire study area was 75.9 per cent with Kappa of 0.59 followed by 75.1 per cent ( Kappa: 0.58 ) of agricultural landscape (b). In pockets of cultivation context(c) accuracy was higher at 79.2 per cent ( Kappa: 0.64 ) possibly due to smaller population. Assessment showed that 1,791 ha of 24,140 ha studied (7.42 %) was under tree cover as per the definitions adopted. Strength of accuracy demonstrated obviously points to the potential of IRS high resolution data combination in setting up procedures to monitor the TOF in Indian context using OBIA approach so as to cater to the evolving demands of resource assessment and monitoring.

Modeling evolution of resistance of sugarcane borer (Lepidoptera: Crambidae) to transgenic Bt corn.

Diatraea saccharalis (F.) (Lepidoptera: Crambidae) is a target pest of transgenic corn expressing Bacillus thuringiensis (Bt) protein, and the first evidence of resistance by D. saccharalis to Cry1Ab corn was detected in a field population in northeast Louisiana in 2004. We used a model of population dynamics and genetics of D. saccharalis to 1) study the effect of interfield dispersal, the first date that larvae enter diapause for overwintering, toxin mortality, the proportion of non-Bt corn in the corn patch, and the area of a crop patch on Bt resistance evolution; and 2) to identify gaps in empirical knowledge for managing D. saccharalis resistance to Bt corn. Increasing, the proportion of corn refuge did not always improve the durability of Bt corn if the landscape also contained sugarcane, sorghum, or rice. In the landscape, which consisted of 90% corn area, 5% sorghum area, and 5% rice area, the durability of single-protein Bt corn was 40 yr when the proportion of corn refuge was 0.2 but 16 yr when the proportion of corn refuge was 0.5. The Bt resistance evolution was sensitive to a change (from Julian date 260 to 272) in the first date larvae enter diapause for overwintering and moth movement. In the landscapes with Bt corn, non-Bt corn, sugarcane, sorghum, and rice, the evolution of Bt resistance accelerated when larvae entered diapause for overwintering early. Intermediate rates of moth movement delayed evolution of resistance more than either extremely low or high rates. This study suggested that heterogeneity in the agrolandscapes may complicate the strategy for managing Bt resistance in D. saccharalis, and designing a Bt resistance management strategy for D. saccharalis is challenging because of a lack of empirical data about overwintering and moth movement.

Impact of host plant connectivity, crop border and patch size on adult Colorado potato beetle retention.

Tagged Colorado potato beetles (CPB), Leptinotarsa decemlineata (Say), were released on potato plants, Solanum tuberosum L., and tracked using a portable harmonic radar system to determine the impact of host plant spatial distribution on the tendency of the pest to remain on the colonized host plant or patch. Results confirmed the long residency time on the host plant and showed that close connection of the plant to neighboring plants hastened dispersal between plants. Tracking walking CPB for over 6 h in small potato plots revealed that all types of mixed borders tested (potato/bare ground, potato/timothy and potato/woodland) acted as a strong barrier and retained beetles within the patch. In another experiment in potato patches surrounded by bare ground borders, tracked walking CPB displayed similar behaviour for up to four days. The distribution of turning angles in the CPB walking paths was not uniform and corresponded to beetles following the edge rows of potato patches in response to the crop border barrier or reversing their direction as they reached the end of a row and therefore a border. Patch size had no or little effect on beetle retention in the patch. The relative distribution of counts of tagged beetles detected among small (16 m2), medium (64 m2) and large size (256 m2) patches of potato four days after initial release remained similar to that of numbers released. Even though mixed crop borders were a strong barrier to walking CPB emigrating from potato patches, the departure rate of beetles over time was high. Results suggest that the effect of mixed borders is largely limited to dispersal by walking and does not apply to beetles leaving host patches by flight. The manipulation of crop borders and patch size seem to have limited potential for the management of CPB emigrating from potato fields.

Habitat linkages in conservation biological control: Lessons from the land–water interface

Terrestrial landscapes, including those with embedded agroecosystems, are a mosaic of cover types varying in size. Creating or maintaining habitats that support natural enemy populations to combat agricultural pests is the primary method of conservation biological control. Non-crop habitats can be managed in an attempt to maximize the exchange of natural enemies with adjacent agroecosystems with the expectation that they will suppress damaging pest outbreaks. Despite this goal, current habitat management relying on natural enemy spillover into crops has been unreliably effective at reducing pest abundance or increasing crop yield. Furthermore, the expansion and intensification of agriculture and changes in global climate patterns threaten the foundations of conservation biological control in future agroecosystems. However, the aquatic–terrestrial interface offers a natural boundary similar to the one between agroecosystems and their neighboring non-crop habitats that can provide useful insights to the challenges facing growers. Research of the exchanges between water and land suggests general biological and physical processes that govern the movement of organisms between disparate habitats. We propose that like aquatic insects moving from water to land, natural enemy dispersal from non-crop donor habitats into recipient crop patches on the landscape is a function of (1) the production of natural enemies in the source habitat which establishes the abundance of organisms that can disperse, (2) how and why mobile natural enemies disperse themselves into neighboring recipient habitats, and (3) the configuration of donor and recipient habitats on the landscape. We suggest that conservation biological control practitioners can focus on these main components of natural enemy production and dispersal to predict the effectiveness of conservation biological control measures and guide their adaptation to future global change.

Spatial and temporal dynamics of Sorghum halepense patches in maize crops

Andújar D, Barroso J, Fernández‐Quintanilla C & Dorado J (2012). Spatial and temporal dynamics of Sorghum halepense patches in maize crops. Weed Research52, 411–420.SummaryPatches of Sorghum halepense of various sizes and densities were artificially created in a maize field. Spatial and temporal patch expansion was monitored during 4 years. The results show that the patches expanded in the same direction of field traffic, with little or no displacement in all the other directions. Apparently, tillage was the main mechanism for dispersal of S. halepense rhizomes, with seeds playing a minor role in dispersal. The spatial growth pattern was characterised by a compact spread of the original patch and by the appearance of small patches at various distances from the primary source, which tended to merge with each other and with the original source. This type of pattern indicates an ‘infiltration’ invasion strategy. The initial patch size significantly influenced their growth rate; small patches grew proportionately faster than larger patches. The initial plant density had a significant influence on the temporal dynamics; low density patches increased up to 130 shoots m−2, the same upper level reached by high density patches. Sorghum halepense seems to be an excellent target for site‐specific weed management (SSWM), with compact patches expanding slowly and in a highly predictable way. However, new foci detached from the original source can expand quickly and could invade the whole field in a relatively short time. Early detection and management of small patches should contribute to improve SSWM practices.

Airborne multi-spectral imagery for mapping cruciferous weeds in cereal and legume crops

Cruciferous weeds are competitive broad-leaved species that cause losses in winter crops. In the present study, research on remote sensing was conducted on seven naturally infested fields located in Cordoba and Seville, southern Spain. Multi-spectral aerial images (four bands, including blue (B), green (G), red (R) and near-infrared bands) taken in April 2007 were used to evaluate the feasibility of mapping cruciferous patches (Diplotaxis spp. and Sinapis spp.) in winter crops (wheat, broad bean and pea) and compare the accuracy of different supervised classification methods (vegetation indices, maximum likelihood and spectral angle mapper). The best classification method was selected to develop site-specific cruciferous treatment maps. Cruciferous patches were efficiently discriminated with red/blue (R/B) and blue/green (B/G) vegetation indices and the maximum likelihood classifier. At all of the locations, the accuracy of the results obtained from the spectral angler mapper was relatively low. The cruciferous weed-classified imagery of each location were created according to the method that provided the best discrimination results and were used to obtain site-specific treatment maps for in-season post-emergence control measures or herbicide applications for subsequent years. By applying the site-specific treatment maps, herbicide savings from 71.7 to 95.4% for the no-treatment areas and from 4.3 to 12% for the low-dose herbicide were obtained.