Even-sowing pattern strategies for a low-input agricultural system in forage maize

The objective of this study was to verify the effectiveness of new patterns of sowing and to achieve a low-input organic system in two different environments (northern and southern Europe). The study was motivated by the hypothesis that more even sowing patterns (triangular and square) would significantly enhance the growth and yield of forage maize under widely varying conditions, compared with traditional mechanised rectangular seed patterns. An experiment was conducted in Madrid and duplicated in Copenhagen during 2010. A random block design was used with a 2 × 2 factorial arrangement based on two seed-sowing patterns: traditional (rectangular) and new (even) and two weed-management conditions (herbicide use and a low-input system). In both weed-management conditions and locations, the production of aerial maize biomass was greater for the new square seed patterns. In addition, the new pattern showed a greater effectiveness in the control of weeds, both at the initial crop stages (36 and 33% fewer weeds m-2 at the 4- and 8-leaf stages, respectively, in the Copenhagen field experiment) and at the final stage. The final weed biomass for the new pattern was 568 kg ha-1 lower for the Copenhagen experiment and 277 kg ha-1 lower in Madrid field experiments. In the light of these results, the new pattern could potentially reduce the use of herbicides. The results of the experiments support the hypothesis formulated at the beginning of this study that even-sowing patterns would be relatively favourable for the growth and yield of the maize crop. In the near future, new machinery could be used to achieve new seed patterns for the optimisation of biomass yield under low-input systems. This approach is effective because it promotes natural crop-weed competition.

Availability and uptake of trace elements in a forage rotation under conservation and plough tillage

After 14 years under conventional plough tillage (CT) or conservation minimum tillage (MT), the soil available Al, Fe, Mn, Cu and Zn (0-5, 5-15 and 15-30 cm layers) and their plant uptake were evaluated during two years in a ryegrass-maize forage rotation in NW Spain (t emperate-humid region). The three-way ANOVA showed that trace element concentrations in soil were mainly influenced by sampling date, followed by soil depth and tillage system (35-73 %, 7-58 % and 3- 11 % of variance explained, respectively). Excepting for Fe (CT) and Al (CT and MT), the elemental concentrations decreased with depth, the stratification being stronger under MT. For soil available Al, Fe, Mn and Cu, the concentrations were higher in CT than in MT (5-15 and 15-30 cm layers) or were not affected by tillage system (0-5 cm). In contrast, the available Zn contents were higher in MT than CT at the soil surface and did not differ in deeper layers. The concentration of Al, Fe and Cu in crops were not influenced by tillage system, which explain 22 % of Mn variance in maize (CT > MT in the more humid year) and 18 % of Zn variance in ryegrass (MT > CT in both years). However, in the summer crop (maize) the concentrations of Fe, Mn and Zn tended to be higher in MT than in CT under drought conditions, while the opposite was true in the year without water limitation. Therefore, under the studied conditions of climate, soil, tillage and crop rotation, little influence of tillage system on crop nutritive value would be expected. To minimize the potential deficiency of Zn (maize) and Cu (maize and ryegrass) on crop yields the inclusion of these micro-nutrients in fertilization schedule is reco mmended, as well as liming to alleviate Al toxicity on maize crops.

CSM-IXIM: A New Maize Simulation Model for DSSAT Version 4.5

Th e CERES-Maize model is the most widely used maize (Zea mays L.) model and is a recognized reference for comparing new developments in maize growth, development, and yield simulation. Th e objective of this study was to present and evaluate CSMIXIM, a new maize simulation model for DSSAT version 4.5. Code from CSM-CERES-Maize, the modular version of the model, was modifi ed to include a number of model improvements. Model enhancements included the simulation of leaf area, C assimilation and partitioning, ear growth, kernel number, grain yield, and plant N acquisition and distribution. Th e addition of two genetic coeffi cients to simulate per-leaf foliar surface produced 32% smaller root mean square error (RMSE) values estimating leaf area index than did CSM-CERES. Grain yield and total shoot biomass were correctly simulated by both models. Carbon partitioning, however, showed diff erences. Th e CSM-IXIM model simulated leaf mass more accurately, reducing the CSM-CERES error by 44%, but overestimated stem mass, especially aft er stress, resulting in similar average RMSE values as CSM-CERES. Excessive N uptake aft er fertilization events as simulated by CSM-CERES was also corrected, reducing the error by 16%. Th e accuracy of N distribution to stems was improved by 68%. Th ese improvements in CSM-IXIM provided a stable basis for more precise simulation of maize canopy growth and yield and a framework for continuing future model developments

Real-time image processing for crop/weed discrimination in maize fields

This paper presents a computer vision system that successfully discriminates between weed patches and crop rows under uncontrolled lighting in real-time. The system consists of two independent subsystems, a fast image processing delivering results in real-time (Fast Image Processing, FIP), and a slower and more accurate processing (Robust Crop Row Detection, RCRD) that is used to correct the first subsystem's mistakes. This combination produces a system that achieves very good results under a wide variety of conditions. Tested on several maize videos taken of different fields and during different years, the system successfully detects an average of 95% of weeds and 80% of crops under different illumination, soil humidity and weed/crop growth conditions. Moreover, the system has been shown to produce acceptable results even under very difficult conditions, such as in the presence of dramatic sowing errors or abrupt camera movements. The computer vision system has been developed for integration into a treatment system because the ideal setup for any weed sprayer system would include a tool that could provide information on the weeds and crops present at each point in real-time, while the tractor mounting the spraying bar is moving

Climate Change Impacts on Maize and Dry Bean Yields of smallholder farmers in Honduras

The rotation maize and dry bean provides the main food supply of smallholder farmers in Honduras. Crop model assessment of climate change impacts (2070?2099 compared to a 1961?1990 baseline) on a maize?dry bean rotation for several sites across a range of climatic zones and elevations in Honduras. Low productivity systems, together with an uncertain future climate, pose a high level of risk for food security. The cropping systems simulation dynamic model CropSyst was calibrated and validated upon field trail site at Zamorano, then run with baseline and future climate scenarios based upon general circulation models (GCM) and the ClimGen synthetic daily weather generator. Results indicate large uncertainty in crop production from various GCM simulations and future emissions scenarios, but generally reduced yields at low elevations by 0 % to 22 % in suitable areas for crop production and increased yield at the cooler, on the hillsides, where farming needs to reduce soil erosion with conservation techniques. Further studies are needed to investigate strategies to reduce impacts and to explore adaptation tactics.

Effects of different soil management practices on soil mechanic resistance and seed depth placement in a maize crop in Alentejo, Portugal Mediterranean region.

No tillage, minimum tillage and conventional tillage practices are commonly used in maize crops in Alentejo, affecting soil physic conditions and determining seeders performance. Seeders distribution can be evaluated in the longitudinal and vertical planes. Vertical plane is specified by seeding depth (Karayel et al., 2008). If, in one hand seeding depth uniformity is a goal for all crop establishment , in the other hand, seeders furrow openers depth control is never constant depending on soil conditions. Seed depth uniformity affects crop emergence, Liu et al. (2004) showed an higher correlation between crop productivity and emergence uniformity than with longitudinal plants distribution. Neto et al. (2007) evaluating seed depth placement by measuring maize mesocotyl length under no tillage conditions in 38 farms concluded that 20% of coefficient of variation suggests the need of improvement seeders depth control mechanisms. The objective of this study was to evaluate casual relationships and create spatial variability maps between soil mechanic resistance and vertical distribution under three different soil practices to improve seed depth uniformity.

Automatic detection of crop rows in maize fields with high weeds pressure

This paper proposes a new method, oriented to crop row detection in images from maize fields with high weed pressure. The vision system is designed to be installed onboard a mobile agricultural vehicle, i.e. submitted to gyros, vibrations and undesired movements. The images are captured under image perspective, being affected by the above undesired effects. The image processing consists of three main processes: image segmentation, double thresholding, based on the Otsu’s method, and crop row detection. Image segmentation is based on the application of a vegetation index, the double thresholding achieves the separation between weeds and crops and the crop row detection applies least squares linear regression for line adjustment. Crop and weed separation becomes effective and the crop row detection can be favorably compared against the classical approach based on the Hough transform. Both gain effectiveness and accuracy thanks to the double thresholding that makes the main finding of the paper.

Crop row detection in maize fields inspired on the human visual perception

This paper proposes a new method, oriented to image real-time processing, for identifying crop rows in maize fields in the images. The vision system is designed to be installed onboard a mobile agricultural vehicle, that is, submitted to gyros, vibrations, and undesired movements. The images are captured under image perspective, being affected by the above undesired effects. The image processing consists of two main processes: image segmentation and crop row detection. The first one applies a threshold to separate green plants or pixels (crops and weeds) from the rest (soil, stones, and others). It is based on a fuzzy clustering process, which allows obtaining the threshold to be applied during the normal operation process. The crop row detection applies a method based on image perspective projection that searches for maximum accumulation of segmented green pixels along straight alignments. They determine the expected crop lines in the images. The method is robust enough to work under the above-mentioned undesired effects. It is favorably compared against the well-tested Hough transformation for line detection.

Calibration of WAVE in irrigated maize: fallow vs. cover crops.

Nitrate leaching decreases crop available N and increases water contamination. Replacing fallow by cover crops (CC) is an alternative to reduce nitrate contamination, because it reduces overall drainage and soil mineral N accumulation. A study of the soil N and nitrate leaching was conducted during 5 years in a semi-arid irrigated agricultural area of Central Spain. Three treatments were studied during the intercropping period of maize (Zea mays L.): barley (Hordeum vulgare L.), vetch (Vicia villosa L.), and fallow. Cover crops, sown in October, were killed by glyphosate application in March, allowing direct seeding of maize in April. All treatments were irrigated and fertilised following the same procedure. Soil water content was measured using capacity probes. Soil Nmin accumulation was determined along the soil profile before sowing and after harvesting maize. Soil analysis was conducted at six depths every 0.20m in each plot in samples from 0 to 1.2-m depth. The mechanistic water balance model WAVE was applied in order to calculate drainage and plant growth of the different treatments, and apply them to the N balance. We evaluated the water balance of this model using the daily soil water content measurements of this field trial. A new Matlab version of the model was evaluated as well. In this new version improvements were made in the solute transport module and crop module. In addition, this new version is more compatible with external modules for data processing, inverse calibration and uncertainty analysis than the previous Fortran version. The model showed that drainage during the irrigated period was minimized in all treatments, because irrigation water was adjusted to crop needs, leading to nitrate accumulation on the upper layers after maize harvest. Then, during the intercrop period, most of the nitrate leaching occurred. Cover crops usually led to a shorter drainage period, lower drainage water amount and lower nitrate leaching than the treatment with fallow. These effects resulted in larger nitrate accumulation in the upper layers of the soil after CC treatments.

Role of maize stover incorporation on nitrogen oxide emissions in a non-irrigated Mediterranean barley field.

Aims Agricultural soils in semiarid Mediterranean areas are characterized by low organic matter contents and low fertility levels. Application of crop residues and/or manures as amendments is a cost-effective and sustainable alternative to overcome this problem. However, these management practices may induce important changes in the nitrogen oxide emissions from these agroecosystems, with additional impacts on carbon dioxide emissions. In this context, a field experiment was carried out with a barley (Hordeum vulgare L.) crop under Mediterranean conditions to evaluate the effect of combining maize (Zea mays L.) residues and N fertilizer inputs (organic and/or mineral) on these emissions. Methods Crop yield and N uptake, soil mineral N concentrations, dissolved organic carbon (DOC), denitrification capacity, N2O, NO and CO2 fluxes were measured during the growing season. Results The incorporation of maize stover increased N2O emissions during the experimental period by c. 105 %. Conversely, NO emissions were significantly reduced in the plots amended with crop residues. The partial substitution of urea by pig slurry reduced net N2O emissions by 46 and 39 %, with and without the incorporation of crop residues respectively. Net emissions of NO were reduced 38 and 17 % for the same treatments. Molar DOC:NO 3 − ratio was found to be a robust predictor of N2O and NO fluxes. Conclusions The main effect of the interaction between crop residue and N fertilizer application occurred in the medium term (4–6 month after application), enhancing N2O emissions and decreasing NO emissions as consequence of residue incorporation. The substitution of urea by pig slurry can be considered a good management strategy since N2O and NO emissions were reduced by the use of the organic residue.

In vitro ruminal fermentation of diets containing wheat straw and date pits as forage.

The pH, VFA concentration, total gas and met hane production were determined in the rumen of four Sicilo- Sarde rams fitted with permanent canulas. Rams received a ration that included 1.5 kg DM of oat hay and were supplemented with one of four concentrates: CC (10% barley, 43.3% corn, 25% wheat bran, 17.7% soybean meal, 4% sheep Vitamin and Mineral Mixture (VMM)), SC (66% white sorghum, 30% faba, 4% sheep VMM); TC (71% triticale, 18% faba, 7%, soybean meal, 4% VMM) or BC (71.5% barley, 17.5% faba, 7% soybean meal and 4% VMM). 50 ml samples were taken before, 2, 5 and 8 hours after the morning meal. Total gas was determined on rumen content before the morning meal. The rumen pH was statistically different (P<0.05) before and 2 hours after the morning meal among concentrates feed. It was in favour of TC and BC (P<0.05) concentrates but was comparable at the end of the day. The concentration of VFA was significantly higher (P<0.05) for diets TC and BC following the meal and became comparable among concentrates thereafter. The proportion of acetate and butyrate acids evolved in the same way during the day regardless of the regimen. The total volu me of gas was different (P<0.05) among diets, the BC showed the highest value (87.00±17.29 ml) while the lowest value was found in the TC concentrate (56.58±13.06 ml). The CH4 production for the BC was significantly different (P<0.05) from that of TC. Quantities produced by the CC and SC were similar (22.08±4.18vs . 21.16±3.21).

Effects of forage type on diversity in bacterial pellets isolated from liquid and solid phases of the rumen content in sheep and goats.

Two sheep and two goats, fitted with a ruminal cannula, received two diets composed of 30% concentrate and 70% of either alfalfa hay (AL) or grass hay (GR) as forage in a two-period crossover design. Solid and liquid phases of the rumen were sampled from each animal immediately before feeding and 4 h post-feeding. Pellets containing solid associated bacteria (SAB) and liquid associated bacteria (LAB) were isolated from the corresponding ruminal phase and composited by time to obtain 2 pellets per animal (one SAB and one LAB) before DNA extraction. Denaturing gradient gel electrophoresis (DGGE) analysis of 16S ribosomal DNA was used to analyze bacterial diversity. A total of 78 and 77 bands were detected in the DGGE gel from sheep and goats samples, respectively. There were 18 bands only found in the pellets from sheep fed AL-fed sheep and 7 found exclusively in samples from sheep fed the GR diet. In goats, 21 bands were found only in animals fed the AL diet and 17 were found exclusively in GR-fed ones. In all animals, feeding AL diet tended (P < 0.10) to promote greater NB and SI in LAB and SAB pellets compared with the GR diet. The dendrogram generated by the cluster analysis showed that in both animal species all samples can be included in two major clusters. The four SAB pellets within each animal species clustered together and the four LAB pellets grouped in a different cluster. Moreover, SAB and LAB clusters contained two clear subclusters according to forage type. Results show that in all animals bacterial diversity was more markedly affected by the ruminal phase (solid vs. liquid) than by the type of forage in the diet.

Cover crops effect on farm benefits and nitrate leaching: Linking economic and environmental analysis.

Introducing cover crops (CC) interspersed with intensively fertilized crops in rotation has the potential to reduce nitrate leaching. This paper evaluates various strategies involving CC between maize and compares the economic and environmental results with respect to a typical maize?fallow rotation. The comparison is performed through stochastic (Monte-Carlo) simulation models of farms? profits using probability distribution functions (pdfs) of yield and N fertilizer saving fitted with data collected from various field trials and pdfs of crop prices and the cost of fertilizer fitted from statistical sources. Stochastic dominance relationships are obtained to rank the most profitable strategies from a farm financial perspective. A two-criterion comparison scheme is proposed to rank alternative strategies based on farm profit and nitrate leaching levels, taking the baseline scenario as the maize?fallow rotation. The results show that when CC biomass is sold as forage instead of keeping it in the soil, greater profit and less leaching of nitrates are achieved than in the baseline scenario. While the fertilizer saving will be lower if CC is sold than if it is kept in the soil, the revenue obtained from the sale of the CC compensates for the reduced fertilizer savings. The results show that CC would perhaps provide a double dividend of greater profit and reduced nitrate leaching in intensive irrigated cropping systems in Mediterranean regions.

Spatial variability of seed depth placement of maize under no tillage.

Among the various factors that contribute towards producing a successful maize crop, seed depth placement is a key determinant, especially in a no-tillage system. The main objective of this work was to evaluate the spatial variability of seed depth placement and crop establishment in a maize crop under no-tillage conditions, using precision farming technologies. The obtained results indicate that seed depth placement was significantly affected by soil moisture content, while a very high coefficient of variation of 39% was found for seed depth. Seeding depth had a significant impact on mean emergence time and percentage of emerged plants. Shallow average depth values and the high coefficient of variation suggest a need for improvement in controlling the seeder sowing depth.

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.