Temperature in relation to phosphorus nutrition in Chinese cabbage

Chinese cabbage plants [Brassica pekinensis (Lour) Rupr. cv. Nagaoka 50] were cultivated experimentally for two years (1993 and 1994) using a semi‐forcing technique of floating rowcovers, polyethylene (T₁), polypropylene (T₂), versus no floating row‐covers, control (T₀). Five samplings were made, taking four plants per each replication and total phosphorus (P) (P_total), inorganic P (P_i), and calculated organic P (P_org) were determined as well as foliar acid phosphatase activity (FAPA). The aerial and root temperatures of the treatments T₁ and T₂ exceeded those of T₀. The P_total concentration showed no significant variations with treatment, whereas the P_i concentrations increased in T_i and T₂ and P_org decreased in both treatments with respect to T₀. The FAPA was influenced a similar way as P_i, raising with temperature. The contents (mg plant^‐1) of P_tolal, P_i, and P_org were greater in T₁ and T₂ than in T₀, and this could be due to the fact that the highest temperatures (root and aerial zone) generated by the plastic rowcovers favored the absorption of P, thereby boosting FAPA and the fresh and dry weights, and yield.     

A new Aphidius Nees species (Hymenoptera,Braconidae), a parasitoid of Acyrthosiphon malvae (Mosley, 1841) in Europe

Phytoparasitica - Here we describe Aphidius erodii sp. n. associated with Acyrthosiphon malvae (Mosley, 1841), which is a pest on herbaceous ornamental Geranium and Pelargonium plants, but also an...     

Impact of city historical management on soil organic carbon stocks in Paris (France)

Journal of Soils and Sediments - Assessments of urban soil organic carbon stock (SOCS) are scant because citywide data are scarce. Moreover, we do not know which factors mostly explain SOCS spatial...     

Does resource complementarity or prevention of lodging contribute to the increased productivity of rice varietal mixtures in Yunnan,China?

We conducted an experiment in Gejiu, Yunnan Province of China during the wet season of 2002 to examine the importance of resource complementarity and prevention of lodging in the fast-spreading practice of growing rice varietal mixtures in China to suppress rice blast disease and its associated damage. The hybrid rice variety Shanyou 63 and the glutinous or sticky rice variety Huangkenuo were used to study intra- and inter-varietal competition in the rice intercropping system. The experiment was laid out as a randomized complete block design in four replications with pure stands and mixtures of both varieties at different total planting densities and mixing ratios. In half of the pure stand treatments of glutinous rice a metal frame was installed to prevent lodging. The results showed that resource complementarity between the two rice varieties, resulting from niche differentiation, was marginal and did not greatly enhance the productivity of the rice varietal mixtures. In contrast, prevention of lodging of glutinous rice was identified as an important additional advantage of growing these rice varieties in mixture.     

Prediction of internal bond strength in a medium density fiberboard process using multivariate statistical methods and variable selection

This paper presents new data mining-based multivariate calibration models for predicting internal bond strength from medium density fiberboard (MDF) process variables. It utilizes genetic algorithms (GA) based variable selection combined with several calibration methods. By adopting a proper variable selection scheme, the prediction performance can be improved because of the exclusion of non-informative variable(s). A case study using real plant data showed that the calibration models based on the process variables selected by GA produced better performance than those without variable selection, with the exception of the radial basis function (RBF) neural networks model. In particular, the calibration model based on supervised probabilistic principal component analysis (SPPCA) yielded better performance (only when using GA) than partial least squares (PLS), orthogonal-PLS (O-PLS), and radial basis function neural networks models. The SPPCA model benefits most from the use of GA-based variable selection in this case study. N. André and H.-W. Cho equally contributed to this work.     

A comparison of crop data measured by two commercial sensors for variable-rate nitrogen application

Nitrogen (N) fertilizer rates applied spatially according to crop requirements can improve the efficiency of N use. The study compares the performance of two commercial sensors, the Yara N-Sensor/FieldScan (Yara International ASA, Germany) and the GreenSeeker (NTech Industries Inc., Ukiah, California, USA), for assessing the status of N in spring wheat (Triticum aestivum L.) and corn (Zea mays L.). Four experiments were conducted at different locations in Quebec and Ontario, Canada. The normalized difference vegetation index (NDVI) was determined with the two sensors at specific growth stages. The NDVI values derived from Yara N-Sensor/FieldScan correlated with those from GreenSeeker, but only at the early growth stages, where the NDVI values varied from 0.2 to 0.6. Both sensors were capable of describing the N condition of the crop or variation in the stand, but each sensor had its own sensitivity characteristics. It follows that the algorithms developed with one sensor for variable-rate N application cannot be transferred directly to another sensor. The Yara N-Sensor/FieldScan views the crop at an oblique angle over the rows and detects more biomass per unit of soil surface compared to the GreenSeeker with its nadir (top-down) view of the crop. The Yara N-Sensor/FieldScan should be used before growth stage V5 for corn during the season if NDVI is used to derive crop N requirements. GreenSeeker performed well where NDVI values were >0.5. However, unlike GreenSeeker, the Yara N-Sensor/FieldScan can also record spectral information from wavebands other than red and near infrared, and more vegetation indices can be derived that might relate better to N status than NDVI.     

Detection of Genetically Modified Soy in Doughs and Cookies

In many countries, including the European Union member states, Switzerland, Australia, New Zealand, and Japan, legislation has been set up for labeling of genetically modified organisms (GMOs) in food and feed products. To comply with these regulations, reliable detection methods are necessary. If the detection is based on DNA, a GMO analysis may contain several steps where qualitative and quantitative species-specific, GMO screening, GMO construct, and GMO line-specific polymerase chain reactions (PCRs) are used. A limit of detection (LOD) thereby defines to what extent a target molecule may be detected in a sample. In this study, cookies were made with variable levels of a soy sample containing 2 wt% Roundup Ready soy. For all PCRs described, detection limits based on dilution series and practical LODs were determined. The practical LODs are used to determine to what extent a GMO ingredient may be detected in a real food product. Results reveal that, due to the baking process, the overall DNA fragment length is reduced, rendering GMO analyses more difficult. Furthermore, Roundup Ready soy line-specific and real-time quantitative PCR are less sensitive than GMO screening PCRs, whereas just these PCRs are crucial in the decision-making process regarding the presence of GMOs in a food product. Moreover, high standard deviations and errors render the precise quantification of GMOs difficult.     

Identifying risk factors for European stone fruit yellows from a survey

ABSTRACT European stone fruit yellows (ESFY) is becoming a major economic problem for Prunus growers in Europe. The causal agent ("Candidatus Phytoplasma prunorum") and its vector (Cacopsylla pruni) have been identified, but the present knowledge of the risk factors for this disease relies, at best, on specific experiments. To assess the relative significance of several factors correlated with ESFY incidence in the field, an exhaustive survey was performed on apricot and Japanese plum orchards in the Crau plain (France). After a preliminary multivariate exploration of the data, we used a logistic regression model to analyze and predict the cumulative number of diseased trees on the basis of a set of quantitative (age, planting density, and area of the orchard) and categorical variables (species, cultivar, and rootstock). Because of the nature of the data, we used an overdispersed binomial model and we developed a parametric bootstrap procedure based on the beta-binomial distribution to obtain confidence intervals. Our results indicated that the age, species, and cultivar of the scion were the major factors explaining the observed number of diseased trees. The planting density and the rootstocks used in the zone under study were less significant, and the area of the orchard had no effect. The residuals of the model showed that some explanatory variables had not been taken into account, because part of the remaining variability could be explained by a grower effect. The spatial distribution of the residuals suggested that one of the reasons for this grower effect was the correlation between orchards closer than 100 m, possibly caused by the flight behavior of infectious vectors.     

Scaling population density to body size in rocky intertidal communities

Interspecific comparisons of animal population density to body size has been the subject of active research in the last decade, especially for terrestrial animals when considering particular taxa or taxonomic assemblages. Studies of rocky intertidal communities showed that animal population density scales with body size to the -0.77 power. This relation held within local communities representing a broad array of animal taxa and was not affected by a dramatic alteration in the network of between-species interactions, as revealed by two long-term human exclusion experiments.     

Issues and challenges in landscape models for agriculture: from the representation of agroecosystems to the design of management strategies

Context

Agroecosystems produce food and many other services that are crucial for human well-being. Given the scales at which the processes underlying these services take place, agricultural landscapes appear as appropriate spatial units for their evaluation and management. The design of sustainable agricultural landscapes that value these services has thus become a pressing issue but faces major challenges stemming from the diversity of processes, their interactions and the number of scales at stake. Agricultural landscape modelling can provide a key contribution to this design but must still overcome several difficulties to offer reliable tools for decision makers.

Objectives

Our study aimed at shedding light on the main scientific and technical difficulties that make the building of landscape models that may efficiently inform decision-makers a complex task, as well as translating them in terms of challenges that can be further investigated and discussed.

Methods

We examine current issues and challenges and indicate future research needs to overcome the scientific and technical obstacles in the development of useful agricultural landscape models.

Results

We highlight research perspectives to better couple landscape patterns and process models and account for feedbacks, integrate the decisions of multiple stakeholders, consider the spatial and temporal heterogeneity of data and processes, explore alternative landscape organisations and assess multiobjective performance.

Conclusion

Coping with the issues and challenges discussed in this paper should improve our understanding of agroecosystems and give rise to new hypotheses, thereby informing future research.