Montserrat Jurado-Expósito

Spatial variability of agricultural soil parameters in southern Spain

Spatial patterns for seven soil chemical properties and textures were examined in two fields in southern Spain (Monclova and Caracol, province of Seville, Andalusia) in order to identify their spatial distribution for the implementation of a site-specific fertilization practice. Two sampling grids of 35×20 and 35×35 m were established in Caracol and Monclova, respectively. Fourteen and eight georeferenced soil samples per hectare were collected at two depths (0–0.1 and 0.25–0.35 m) in early November 1998 before fertilizing and planting the winter crop. Data were analyzed both statistically and geostatistically on the basis of the semivariogram. The spatial distribution model and spatial dependence level varied both between and within locations. Some of the soil properties showed lack of spatial dependence at both depths and at the chosen interval (lag h). Such was the case for clay, organic matter and NH4 at Monclova; and clay and NH4 at Caracol. Bray P and exchangeable K showed a strong patchy distribution at any field and depth. It is important to know the spatial dependence of soil parameters, as management parameters with strong spatial dependence (patchy distribution) will be more readily managed and an accurate site-specific fertilization scheme for precision farming more easily developed.

Multi-species weed spatial variability and site-specific management maps in cultivated sunflower

Abstract Geostatistical techniques were used to describe and map weed spatial distribution in two sunflower fields in Cabello and Monclova, southern Spain. Data from the study were used to design intermittent spraying strategies. Weed species, overall infestation severity (IS) index, and spatial distribution varied considerably between the two sites. Weed species displayed differences in spatial dependence regardless of IS. The IS mapping of each single weed and of the overall infestation was achieved by kriging, and site-specific application maps were then drawn based on the multi-species weed map and the estimated economic threshold (ET). Herbicide treatment was assumed to be needed for an overall IS score of 2 or 3, and the infested “area exceeding the economic threshold” was determined. The overall weed-infested area varied considerably between locations. About 99 and 38% of the total area was moderately infested (IS ≥ 2) at Monclova and Cabello, respectively. Therefore, if a given herbicide were applied just to the areas exceeding the ET, a significant herbicide saving would be realized in Cabello but not in Monclova. A multi-species spatial analysis provides an opportunity to make site-specific management recommendations from a map of the distribution of IS of the total infestation. Furthermore, only in fields with hard-to-control weed species (e.g., nodding broomrape and corn caraway) would site-specific herbicide application maps developed from total weed infestations need to be complemented with targeted site-specific herbicide treatments to prevent further spread of these species, although their IS might be low. Nomenclature: Glyphosate; Bristly oxtongue, Picris echioides L. PICEC; catchweed bedstraw, Gallium aparine L. GALAP; common lambsquarters, Chenopodium album L. CHEAL; corn caraway, Ridolfia segetum Morris, CRYRI; cowcockle, Vaccaria pyramidata Medik. VAAPY; European heliotrope, Heliotropium europaeum L. HEOEU; field bindweed, Convolvulus arvensis L. CONAR; littleseed canarygrass, Phalaris paradoxa L. PHAPA; nodding broomrape, Orobanche cernua Loefl. ORACE; prostrate knotweed, Polygonum aviculare L. POLAU; rapeseed, Brassica napus L.; sunflower, Helianthus annuus L.; tumble pigweed, Amaranthus albus L. AMAAL; wild mustard, Sinapis arvensis L. SINAR

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 Córdoba 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.

Spectral discrimination of wild oat and canary grass in wheat fields for less herbicide application

Wheat, Triticum durum L, is a major cereal crop in Spain with over five million ha grown annually. Wild oat, Avena sterilis L., and canary grass, Phalaris spp., are distributed only in patches in wheat fields but herbicides are applied over entire fields, thus leading to over-application and unnecessary pollution. To reduce herbicide application, site-specific management techniques based on weed maps are being developed to treat only weed patches. Intensive weed scouting from the ground is time-consuming and expensive, and it relies on estimates of weeds at unsampled points. Remote sensing of weed canopies has been shown to be a more efficient alternative. The principle of weed remote sensing is that there are differences in the spectral reflectance between weeds and crops. To test this principle, we studied spectral signatures taken on the ground in the visible and near-infrared windows for discriminating wheat, wild oat and canary grass at their last phenological stages. Late-season phenological stages included initial seed maturation through advanced maturation for weeds, and initial senescence to senescent for wheat. Spectral signatures were collected on eight sampling dates from April 28 through May 26 using a handheld field spectroradiometer. A stepwise discriminant analysis was used to detect differences in reflectance and to determine the accuracy performance for a species classification as affected by their phenological stage. Four scenarios or classification sets were considered: wheat-wild oat-canary grass, with each species represented by a different group of spectra; wheat and grass weeds, combining the two weed species into one spectral group; wheat and wild oat with each represented as a single group, and finally, wheat and canary grass. Our analysis achieved 100% classification accuracy at the phenological stages of initial seed maturation, and green and advanced seed maturation and partly green for weeds and wheat, respectively, between the dates of April 28 and May 6. Furthermore, we reduced the number of hyperspectral wavelengths to thirteen out of 50. Multispectral analysis also showed that broad wavebands corresponding to those of QuickBird satellite imagery discriminated wild oat, canary grass and wheat at the same phenological stages and dates. Our findings are very useful for determining the timeframe during which future multispectral QuickBird satellite images will be obtained and the concrete wavelengths that should be used in case of using airborne hyperspectral imaging. Accurate and timely mapping of the spatial distribution of weeds is a key element in achieving site-specific herbicide applications for reducing spraying volume of herbicides and costs.

Broad bean and lentil seed treatments with imidazolinones for the control of broomrape (Orobanche crenata).

Studies were conducted from 1993 to 1995 in Southern Spain to determine the feasibility of controlling broomrape ( Orobanche crenata Forsk.) in broad bean ( Vicia faba L.) and lentil ( Lens culinaris L.) by treating seeds with imazethapyr and imazapyr. In the broad bean, soaking for 5 min in 0·01–0·1% herbicide solutions or coating at 20–40 g ha −1 (seed sowing rate 160 kg ha −1 ) with imazethapyr (Pursuit-10) did not affect seed germination and crop growth, and resulted in 60–80% broomrape control. Furthermore, broad bean seeds treated with imazethapyr followed by an additional late post-emergence application of imazapyr (Arsenal-25) at 5 g ha −1 resulted in excellent broomrape control (>95%). Similarly, lentil seed treatments with imazapyr by coating seeds at rates equivalent to 5–10 g ha −1 or by soaking for 5 min in 0·25% solutions did not affect germination or crop growth, and controlled 85–95% of broomrape. As a result, with broomrape-efficient herbicide treatments, crop biomass/seed yield increased as compared to broomrape-infested, non-treated controls. Herbicide seed treatments with imazapyr in broad bean and with imazethapyr in lentil were less well tolerated and were less effective in controlling broomrape than treatments with imazethapyr and imazapyr, respectively.

Applying neural networks to hyperspectral and multispectral field data for discrimination of cruciferous weeds in winter crops

In the context of detection of weeds in crops for site-specific weed control, on-ground spectral reflectance measurements are the first step to determine the potential of remote spectral data to classify weeds and crops. Field studies were conducted for four years at different locations in Spain. We aimed to distinguish cruciferous weeds in wheat and broad bean crops, using hyperspectral and multispectral readings in the visible and near-infrared spectrum. To identify differences in reflectance between cruciferous weeds, we applied three classification methods: stepwise discriminant (STEPDISC) analysis and two neural networks, specifically, multilayer perceptron (MLP) and radial basis function (RBF). Hyperspectral and multispectral signatures of cruciferous weeds, and wheat and broad bean crops can be classified using STEPDISC analysis, and MLP and RBF neural networks with different success, being the MLP model the most accurate with 100%, or higher than 98.1%, of classification performance for all the years. Classification accuracy from hyperspectral signatures was similar to that from multispectral and spectral indices, suggesting that little advantage would be obtained by using more expensive airborne hyperspectral imagery. Therefore, for next investigations, we recommend using multispectral remote imagery to explore whether they can potentially discriminate these weeds and crops.

A multi-objective neural network based method for cover crop identification from remote sensed data

One of the objectives of conservation agriculture to reduce soil erosion in olive orchards is to protect the soil with cover crops between rows. Andalusian and European administrations have developed regulations to subsidise the establishment of cover crops between rows in olive orchards. Current methods to follow-up the cover crops systems by administrations consist of sampling and on ground visits of around 1% of the total olive orchards surface at any time from March to late June. This paper outlines a multi-objective neural network based method for the classification of olive trees (OT), bare soil (BS) and different cover crops (CC), using remote sensing data taken in spring and summer. The main findings of this paper are: (1) the proposed models performed well in all seasons (particularly during the summer, where only 48 pixels of CC are confused with BS and 10 of BS with CC with the best model obtained. This model obtained a 97.80% of global classification, 95.20% in the class with the worst classification rate and 0.9710 in the KAPPA statistics), and (2) the best-performing models could potentially decrease the number of complaints made to the Andalusian and European administrations. The complaints in question concern the poor performance of current on-ground methods to address the presence or absence of cover crops in olive orchards.

Discriminating cropping systems and agro-environmental measures by remote sensing

The agrarian policy of the European Union tends to support sustainable agriculture, subsidising only cropping systems that are implemented with specific agro-environmental measures. These actions require a precise follow-up of the crops and of the agricultural practices over a large surface. To that end, remote-sensing techniques are unique and cost-effective. We developed here a digital land cover classification in the Mediterranean dryland, mapping and assessing the main cropping systems and some agro-environmental measures such as cover crops in olive orchards and crop stubble for reducing soil erosion. We analysed a high spatial resolution satellite image (QuickBird) taken in early summer around Montilla, southern Spain. Images of the four broad wavebands, six band ratios and three vegetation indices were extracted from the satellite image and studied for the discrimination of nine land covers. The classified regions were determined by applying adequate boundary digital values to the selected images. Our results show that the land covers were discriminated with an overall accuracy of about 90%. Images of the normalised difference vegetation index and the ratio vegetation index discriminated between vegetation and non-vegetation zones. The visible wavebands discriminated roadside trees and herbaceous crops, and the near-infrared waveband highways and urban soil plus bare soil. The ratios blue/green and red/green were useful for distinguishing non-burnt stubble. The burnt stubble area was discriminated through the adapted burnt area index. Olive orchards were classified once the regions of vegetation, non-vegetation and non-burnt stubble were extracted. This technology will be a useful tool of agroecology control for the administration and will be a substitute for the current follow-up of cropping systems by ground visits. It can also be used on a farm level in order to help farmers and technicians to make decisions about the management of sustainable agricultural practices.

Pronamide Applied to Sunflower Seeds for Orobanche cumana Control

Field and laboratory studies were conducted from 1993 to 1997 to determine the feasibility of controlling nodding broomrape in sunflower by treating crop seeds with pronamide. Soaking sunflower seeds for 5 min in 50% pronamide solution or coating at the equivalent of 2 kg/ha with pronamide did not impair seed germination or seedling growth, and controlled nodding broomrape 49 to 68% and 51 to 77%, respectively, up to 105 d after planting. Studies of the effect of treated sunflower seeds on germination and seedling growth, at several time intervals after the herbicide application (0, 30, 60, and 90 d after treatment [DAT]), were conducted. Soaking with 50% solution or coating at 2 kg/ha reduced seedling growth by 20 and 24% 60 DAT, respectively, compared with the control. Nomenclature: Pronamide; nodding broomrape, Orobanche cumana Wallr. #3 ORACU; sunflower, Helianthus annuus L. Additional index words: Herbicide seed treatment, parasitic weed. Abbreviations: DAP, days after planting; DAT, days after treatment; HST, herbicide seed treatment.

Sunflower yield related to multi-temporal aerial photography, land elevation and weed infestation

This study investigated the relationships between sunflower yield and crop multi-temporal spectral data obtained from aerial photographs, land elevation and the presence of Ridolfia segetum weed. Conventional-color and color-infrared airborne photographs were taken at three dates corresponding to the vegetative, flowering and senescent crop stages. Descriptive and statistical methods were applied to every spatial variable to extract the influence of each component on the sunflower yield variability. Principal components and regression models were used to explore the potential of the multi-spectral variables from the airborne photographs to predict the sunflower yield map at every studied date. Higher sunflower yield was found in areas with lower elevation. These areas were also predominantly free of weed infestation. The Normalized Difference Vegetation Index derived from the image taken at crop vegetative stage was strongly correlated to crop yield. A very poor correlation was detected between the sunflower yield and all the multi-spectral variables studied in the flowering and the senescence crop stages. A map with three zones of yield was predicted with 67.81% of overall accuracy using the stepwise-model equation formed by the green and red bands and the two vegetation indices obtained at vegetative crop stage. The selected multi-spectral data taken in early season (mid-May), plus the additional knowledge of weed presence and field elevation, could provide valuable spatial information to estimate the yield crop variability in the studied fields. This estimation might aid in the development of adequate spatially variable management strategies in the months prior to the sunflower harvest.