Luis García-Torres

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

Using remote sensing for identification of late-season grass weed patches in wheat

Abstract Field research was conducted to determine the potential of hyperspectral and multispectral imagery for late-season discrimination and mapping of grass weed infestations in wheat. Differences in reflectance between weed-free wheat and wild oat, canarygrass, and ryegrass were statistically significant in most 25-nm-wide wavebands in the 400- and 900-nm spectrum, mainly due to their differential maturation. Visible (blue, B; green, G; red, R) and near infrared (NIR) wavebands and five vegetation indices: Normalized Difference Vegetation Index (NDVI), Ratio Vegetation Index (RVI), R/B, NIR-R and (R − G)/(R + G), showed potential for discriminating grass weeds and wheat. The efficiency of these wavebands and indices were studied by using color and color-infrared aerial images taken over three naturally infested fields. In StaCruz, areas infested with wild oat and canarygrass patches were discriminated using the indices R, NIR, and NDVI with overall accuracies (OA) of 0.85 to 0.90. In Florida–West, areas infested with wild oat, canarygrass, and ryegrass were discriminated with OA from 0.85 to 0.89. In Florida–East, for the discrimination of the areas infested with wild oat patches, visible wavebands and several vegetation indices provided OA of 0.87 to 0.96. Estimated grass weed area ranged from 56 to 71%, 43 to 47%, and 69 to 80% of the field in the three locations, respectively, with per-class accuracies from 0.87 to 0.94. NDVI was the most efficient vegetation index, with a highly accurate performance in all locations. Our results suggest that mapping grass weed patches in wheat is feasible with high-resolution satellite imagery or aerial photography acquired 2 to 3 wk before crop senescence. Nomenclature: Wild oat (Avena fatua L. AVEFA and A. sterilis L. AVEST); canarygrass (Phalaris paradoxa L. PHAPA and P. minor Retz PHAMI); ryegrass, Lolium rigidum Gaudin LOLRI; wheat, Triticum durum L. ‘Mexicali.’

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.

SEMAGI — an expert system for weed control decision making in sunflowers☆

Abstract To use herbicides efficiently, decision makers must estimate when weed populations exceed economic treatment thresholds. An interactive microcomputer program named SEMAGI has been developed for sunflower ( Helianthus annuus L.) to evaluate the potential yield reduction from multi-species weed infestations and from the parasitic weed broomrape ( Orobanche cernua/O. cumana ), and to determine the appropriate selection of herbicides. It combines relational databases on herbicides, weeds and their interaction. Originally, 34 weed species and twenty-six herbicides were introduced specifying each weed/herbicide efficacy combination. For other agricultural situations, SEMAGI permits the introduction of new weeds (up to 80), new herbicides (up to 40) and each herbicide-weed efficacy combination. The expert system processes and selects the herbicide(s) under the constraints of herbicide efficacy data and of a weed-crop competition model. This relates weed-infested crop yield (SY I ), potential weed-free yield (SY F ), weed density (RD) and weed biomass (RBio). The user evaluates the weed infestation by field survey or density counting and the program converts it into equivalent weed biomass. Weed species are classified in three groups according to their final size. A relationship between weed density, weed size and equivalent biomass is established for any weed group. In addition, SEMAGI provides an economic study of any herbicide treatment selected or introduced by the user, based on herbicide treatment cost, expected yield increase from the weed control treatment and sunflower selling price. A computer capable of running MS-DOS or PC-DOS version 2.0 or greater with a minimum of 2 M bytes of RAM is required. This approach should be applicable to other crops.

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.

Spatial distribution and mapping of crenate broomrape infestations in continuous broad bean cropping

Abstract Geostatistical techniques were used to describe and map the spatial distribution of crenate broomrape populations parasitizing broad bean over 6 yr (from 1985 to 1990). In the first year, the spatial distribution was random, but from 1986 to 1989, crenate broomrape populations were clearly aggregated. The crenate broomrape infection severity (IS: number of emerged broomrape m−2) increased every year, from an average of 0.45 in 1985 to 29.4 in 1989, with a slight decrease the following year (IS = 27.4). Spherical functions provided the best fit because the cross-validation criteria were accomplished in all study cases. Kriged estimates were used to draw contour maps of the populations. About 34.3, 43.3, and 74.3% of the field plot surface exhibited an IS ≥ 1 (economic threshold) in 1985, 1986, and 1987, respectively, and nearly 100% of the area exceeded the economic threshold from 1988 to 1990; 1985 and 1986 were key years for control of the parasitic weed population. The percentage of infested area at different IS intervals in each years map obtained by kriging was used to estimate the percentage of yield losses in each infested area (YA) with the equation: YA = A * Ymax * (1 − IS * 0.124), where A is the infested area at a given IS interval and Ymax is the expected broomrape-free broad bean yield. Yield losses under different IS intervals were compared with yield loss attributable to a uniform distribution of crenate broomrape. Results showed that yield loss assuming a uniform distribution of crenate broomrape was clearly overestimated, which is important to avoid overuse of herbicides. Nomenclature: Crenate broomrape, Orobanche crenata Forsk. ORACR; Vicia faba L.

Grassy weeds in winter cereals in Southern Spain.

Abstract A survey of grassy weeds in winter cereal crops was conducted in Andalusia (Spain). The parameters determined for each species were frequency, uniformity, average density, average density of infested fields, relative abundance and the degree of tillering or reshooting. The most frequent and uniformly distributed species was Avena sterilis found in 65% of the fields and in all the geographical areas studied. The most infested region was the province of Jaen. Lolium rigidum was found in 34·29% of the fields, being particularly important in the province of Granada. Phalaris spp. were found mostly in the western part of Andalusia; of these, Phalaris brachystachys was the most frequent (32·86% of the fields) although the most intense infestation was produced by P. paradoxa and P. minor. Of lesser importance and in decreasing order were Cynodon dactylon, Bromus diandrus, Hordeum murinum, Trisetaria panicea, Bromus tectorum and B. madritensis; 15 other species were also observed, but only once.

Control of broomrape (Orobanche cernua) in sunflower (Helianthus annuus L.) with glyphosate.

Abstract Field experiments were conducted in 1986 and 1987 in several locations of Andalusia, Spain, to determine the efficacy of glyphosate for the control of broomrape in sunflower and to examine the crop response to this herbicide under weed-free conditions. Glyphosate effectively controlled broomrape. Sunflower plants with ≈22 leaves and broomrapes at predominant growth stages ‘d’ (bud emergence) and ‘e’ (developed radicles) were the most susceptible to single treatments of glyphosate. Under these conditions, herbicide doses of ⩾ 60 g ha−1 were usually needed to achieve good broomrape control. Alternatively, two applications of 40 g ha−1 or three applications of 20 g ha−1 at ≈12- to 14-day intervals were generally required to control ⩾ 80% of the broomrape. However, sunflower yields with all these treatments were not greater than those of the untreated infested checks: in fact, applications of glyphosate to weed-free sunflower at doses that effectively control broomrape produced phytoxoxicity and reduced yield.

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.