Nutritive value and protein quality of soybean meals according to origin and crop year

Resultados de la investigación sobre el valor nutritivo y calidad de la proteína de la alimentación basada en soja en función del origen y del año de la recolección.

How do various maize crop models vary in their responses to climate change factors?

Comments This article is a U.S. government work, and is not subject to copyright in the United States. Abstract Potential consequences of climate change on crop production can be studied using mechanistic crop simulation models. While a broad variety of maize simulation models exist, it is not known whether different models diverge on grain yield responses to changes in climatic factors, or whether they agree in their general trends related to phenology, growth, and yield. With the goal of analyzing the sensitivity of simulated yields to changes in temperature and atmospheric carbon dioxide concentrations [CO2], we present the largest maize crop model intercomparison to date, including 23 different models. These models were evaluated for four locations representing a wide range of maize production conditions in the world: Lusignan (France), Ames (USA), Rio Verde (Brazil) and Morogoro (Tanzania). While individual models differed considerably in absolute yield simulation at the four sites, an ensemble of a minimum number of models was able to simulate absolute yields accurately at the four sites even with low data for calibration, thus suggesting that using an ensemble of models has merit. Temperature increase had strong negative influence on modeled yield response of roughly 0.5 Mg ha 1 per °C. Doubling [CO2] from 360 to 720 lmol mol 1 increased grain yield by 7.5% on average across models and the sites. That would therefore make temperature the main factor altering maize yields at the end of this century. Furthermore, there was a large uncertainty in the yield response to [CO2] among models. Model responses to temperature and [CO2] did not differ whether models were simulated with low calibration information or, simulated with high level of calibration information.

Automatic expert system based on images for accuracy crop row detection in maize fields

This paper proposes an automatic expert system for accuracy crop row detection in maize fields based on images acquired from a vision system. Different applications in maize, particularly those based on site specific treatments, require the identification of the crop rows. The vision system is designed with a defined geometry and installed onboard a mobile agricultural vehicle, i.e. submitted to vibrations, gyros or uncontrolled movements. Crop rows can be estimated by applying geometrical parameters under image perspective projection. Because of the above undesired effects, most often, the estimation results inaccurate as compared to the real crop rows. The proposed expert system exploits the human knowledge which is mapped into two modules based on image processing techniques. The first one is intended for separating green plants (crops and weeds) from the rest (soil, stones and others). The second one is based on the system geometry where the expected crop lines are mapped onto the image and then a correction is applied through the well-tested and robust Theil–Sen estimator in order to adjust them to the real ones. Its performance is favorably compared against the classical Pearson product–moment correlation coefficient.

Discriminating Crop, Weeds and Soil Surface with a Terrestrial LIDAR Sensor

In this study, the evaluation of the accuracy and performance of a light detection and ranging (LIDAR) sensor for vegetation using distance and reflection measurements aiming to detect and discriminate maize plants and weeds from soil surface was done. The study continues a previous work carried out in a maize field in Spain with a LIDAR sensor using exclusively one index, the height profile. The current system uses a combination of the two mentioned indexes. The experiment was carried out in a maize field at growth stage 12–14, at 16 different locations selected to represent the widest possible density of three weeds: Echinochloa crus-galli (L.) P.Beauv., Lamium purpureum L., Galium aparine L.and Veronica persica Poir.. A terrestrial LIDAR sensor was mounted on a tripod pointing to the inter-row area, with its horizontal axis and the field of view pointing vertically downwards to the ground, scanning a vertical plane with the potential presence of vegetation. Immediately after the LIDAR data acquisition (distances and reflection measurements), actual heights of plants were estimated using an appropriate methodology. For that purpose, digital images were taken of each sampled area. Data showed a high correlation between LIDAR measured height and actual plant heights (R 2 = 0.75). Binary logistic regression between weed presence/absence and the sensor readings (LIDAR height and reflection values) was used to validate the accuracy of the sensor. This permitted the discrimination of vegetation from the ground with an accuracy of up to 95%. In addition, a Canonical Discrimination Analysis (CDA) was able to discriminate mostly between soil and vegetation and, to a far lesser extent, between crop and weeds. The studied methodology arises as a good system for weed detection, which in combination with other principles, such as vision-based technologies, could improve the efficiency and accuracy of herbicide spraying.

Crop rotation and residues influence N2O emission and N efficiency rather than tillage under a rainfed Mediterranean agroecosystem

Conservation tillage and crop rotation have spread during the last decades because promotes several positive effects (increase of soil organic content, reduction of soil erosion, and enhancement of carbon sequestration) (Six et al., 2004). However, these benefits could be partly counterbalanced by negative effects on the release of nitrous oxide (N2O) (Linn and Doran, 1984). There is a lack of data on long-term tillage system study, particularly in Mediterranean agro-ecosystems. The aim of this study was to evaluate the effects of long-term (>17 year) tillage systems (no tillage (NT), minimum tillage (MT) and conventional tillage (CT)); and crop rotation (wheat (W)-vetch (V)-barley (B)) versus wheat monoculture (M) on N2O emissions. Additionally, Yield-scaled N2O emissions (YSNE) and N uptake efficiency (NUpE) were assessed for each treatment.

Influence of Tillage and Liming on N2O emission from a rainfed crop

Nitrous oxide (N2O) is the main greenhouse gas (GHG) produced by agricultural soils due to microbial processes. The application of N fertilizers is associated with an increase of N2O losses. However, it is possible to mitigate these emissions by the introduction of adequate management practices (Snyder et al., 2009). Soil conservation practices (i.e.no tillage, NT) have recently become widespread because they promote several positive effects (increases in soil organic carbonand soil fertility, reduction of soil erosion, etc). In terms of GHG emissions, there is no consensus in the literature on the effects of tillage on N2O. Several studies found that NT can produce greater (Baggs et al., 2003), lower (Malhi et al., 2006) or similar (Grandey et al., 2006) N2O emissions compared to traditional tillage (TT). This large uncertainty is associated with the duration of tillage practices and climatic variability. Liming is widely use to solve problems of soil acidity (Al toxicity, yield penalties, etc). Several studies show a decrease in N2O emissions with liming (Barton et al., 2013) whereas no significant effects or increases were observed in others (Galbally et al., 2010). The aim of this work was to evaluate the effects of tillage (NT vs TT) and liming application or not of Ca-amendment) on N2O emissions from an acid soil during a rainfed crop.

Changes in rumen microbes in sheep in response to dietary forage quality and relationships with fermentation characteristics

An in vitro experiment was carried out using the Hohenheim gas production technique to evaluate 24-h gas production, apparently and truly degraded dry matter (DM), partitioning factor (PF), short chain fatty acids, crude protein (CP) and carbohydrate (CHO) fractionation of grass and multipurpose tree species (MPTS) foliage diets. Four grasses and three MPTS were used to formulate 12 diets of equal mixtures (0.5:0.5 on DM basis) of each grass with each MPTS. In vitro gas production was terminated after 24 h for each diet. True DM degradability was measured from incubated samples and combined with gas volume to estimate PF. Diets had greater (P<0.001) CP (102–183 g/kg DM) content than sole grasses (66–131 g/kg DM) and lower (P<0.001) concentrations of fibre fractions. Contrary to in vitro apparently degraded DM, in vitro truly degraded DM coefficient was greater (P<0.001) in diets (0.63–0.77) than in sole grasses (0.48–0.68). The PF was on average higher in diets than in sole grasses. The proportion of potentially degradable CP fractions (A1, B1, B2 and B3, based on the Cornell Net Carbohydrate and Protein System) in the diets ranged from 971 to 989 g/kg CP. Crude protein fractions, A and B2 were greater in diets but B1 and B3 fractions were less in diets than in sole grasses. A similar trend was also observed in the CHO fractions. Results showed that the nutritive value of the four grasses was improved when MPTS leaves were incorporated into the diet and this could ensure higher productivity of the animals.

Efficiency and environmental indexes to evaluate the sustainability of mineral and organic fertilization in an irrigated melon crop

Melon is traditionally cultivated in fertigated farmlands in the center of Spain with high inputs of water and N fertilizer. Excess N can have a negative impact, from the economic point of view, since it can diminish the production and quality of the fruit, from the environmental point of view, since it is a very mobile element in the soil and can contaminate groundwater. From health point of view, nitrate can be accumulated in fruit pulp, and, in addition, groundwater is the fundamental supply source of human populations. Best management practices are particularly necessary in this region as many zones have been declared vulnerable to NO3- pollution (Directive 91/676/CEE) During successive years, a melon crop (Cucumis melo L.) was grown under field conditions applying mineral and organic fertilizers under drip irrigation. Different doses of ammonium nitrate were used as well as compost derived from the wine-distillery industry which is relevant in this area. The present study reviews the most common N efficiency indexes under the different management options with a view to maximizing yield and minimizing N loss.

Yield, nutrient utilization and soil properties in a melon crop amended with wine-distillery waste

In Spain, large quantities of wine are produced every year (3,339,700 tonnes in 2011) (FAO, 2011) with the consequent waste generation. During the winemaking process, solid residues like grape stalks are generated, as well as grape marc and wine lees as by-products. According to the Council Regulation (EC) 1493/1999 on the common organization of the wine market, by-products coming from the winery industry must be sent to alcohol-distilleries to generate exhausted grape marc and vinasses. With an adequate composting treatment, these wastes can be applied to soils as a source of nutrients and organic matter. A three-year field experiment (2011, 2012 and 2013) was carried out in Ciudad Real (central Spain) to study the effects of wine-distillery waste compost application in a melon crop (Cucumis melo L.). Melon crop has been traditionally cultivated in this area with high inputs of water and fertilizers, but no antecedents of application of winery wastes are known. In a randomized complete block design, four treatments were compared: three compost doses consisted of 6.7 (D1), 13.3 (D2) and 20 t compost ha-1 (D3), and a control treatment without compost addition (D0). The soil was a shallow sandy-loam (Petrocalcic Palexeralfs) with a depth of 0.60 m and a discontinuous petrocalcic horizon between 0.60 and 0.70 m, slightly basic (pH 8.4), poor in organic matter (0.24%), rich in potassium (410 ppm) and with a medium level of phosphorus (22.1 ppm). During each growing period four harvests were carried out and total and marketable yield (fruits weighting <1 kg or visually rotten were not considered), fruit average weight and fruit number per plant were determined. At the end of the crop cycle, four plants per treatment were sampled and the nutrient content (N, P and K) was determined. Soil samplings (0-30 cm depth) were carried before the application of compost and at the end of each growing season and available N and P, as well as exchangeable K content were analyzed. With this information, an integrated analysis was carried out with the aim to evaluate the suitability of this compost as organic amendment.

Ground and airborne level optical sensors: is it possible to estimate maize crop N status?

Adjusting N fertilizer application to crop requirements is a key issue to improve fertilizer efficiency, reducing unnecessary input costs to farmers and N environmental impact. Among the multiple soil and crop tests developed, optical sensors that detect crop N nutritional status may have a large potential to adjust N fertilizer recommendation (Samborski et al. 2009). Optical readings are rapid to take and non-destructive, they can be efficiently processed and combined to obtain indexes or indicators of crop status. However, other physiological stress conditions may interfere with the readings and detection of the best crop nutritional status indicators is not always and easy task. Comparison of different equipments and technologies might help to identify strengths and weakness of the application of optical sensors for N fertilizer recommendation. The aim of this study was to evaluate the potential of various ground-level optical sensors and narrow-band indices obtained from airborne hyperspectral images as tools for maize N fertilizer recommendations. Specific objectives were i) to determine which indices could detect differences in maize plants treated with different N fertilizer rates, and ii) to evaluate its ability to identify N-responsive from non-responsive sites.

The impact of large-scale circulation patterns on summer crop in Iberian Peninsula

Large-scale circulations patterns (ENSO, NAO) have been shown to have a significant impact on seasonal weather, and therefore on crop yield over many parts of the world(Garnett and Khandekar, 1992; Aasa et al., 2004; Rozas and Garcia-Gonzalez, 2012). In this study, we analyze the influence of large-scale circulation patterns and regional climate on the principal components of maize yield variability in Iberian Peninsula (IP) using reanalysis datasets. Additionally, we investigate the modulation of these relationships by multidecadal patterns. This study is performed analyzing long time series of maize yield, only climate dependent, computed with the crop model CERES-maize (Jones and Kiniry, 1986) included in Decision Support System for Agrotechnology Transfer (DSSAT v.4.5).

Examining wheat yield sensitivity to temperature and precipitation changes for a large ensemble of crop models using impact response surfaces

Impact response surfaces (IRSs) depict the response of an impact variable to changes in two explanatory variables as a plotted surface. Here, IRSs of spring and winter wheat yields were constructed from a 25-member ensemble of process-based crop simulation models. Twenty-one models were calibrated by different groups using a common set of calibration data, with calibrations applied independently to the same models in three cases. The sensitivity of modelled yield to changes in temperature and precipitation was tested by systematically modifying values of 1981-2010 baseline weather data to span the range of 19 changes projected for the late 21st century at three locations in Europe.

Designing new cereal cultivars as an adaptation measure using crop model ensembles

To date, crop models have been little used for characterising the types of cultivars suited to a changed climate, though simulations of altered management (e.g. sowing) are often reported. However, in neither case are model uncertainties evaluated at the same time.

Improving Crop Models: Incorporating New Processes, New Approaches, and Better Calibrations

Early ancestors of crop simulation models (De Wit, 1965; Monteith, 1965; Duncan et al., 1967) were born before primitive personal computers were available (e.g. Apple II released in 1977, IBM PC released in 1981). Paleo-computer programs were run in mainframes with the support of punch cards. As computers became more available and powerful, crop models evolved into sophisticated tools summarizing our understanding of how crops operate. This evolution was triggered by the need to answer new scientific questions and improve the accuracy of model simulations, especially under limiting conditions.

Model inter-comparison on crop rotation effects ? an intermediate report

Data of diverse crop rotations from five locations across Europe were distributed to modelers to investigate the capability of models to handle complex crop rotations and management interactions.