Weed manipulation for insect pest management in corn

Populations of insect pests and associated predaceous arthropods were sampled by direct observation and other relative methods in simple and diversified corn habitats at two sites in north Florida during 1978 and 1979. Through various cultural manipulations, characteristic weed communities were established selectively in alternate rows within corn plots.Fall armyworm (Spodoptera frugiperda J. E. Smith) incidence was consistently higher in the weed-free habitats than in the corn habitats containing natural weed complexes or selected weed associations. Corn earworm (Heliothis zea Boddie) damage was similar in all weed-free and weedy treatments, suggesting that this insect is not affected greatly by weed diversity. Only the diversification of corn with a strip of soybean significantly reduced corn earworm damage.In one site, distance between plots was reduced. Because predators moved freely between habitats, it was difficult to identify between-treatment differences in the composition of predator communities. In the other site, increased distances between plots minimized such migrations, resulting in greater population densities and diversity of common foliage insect predators in the weed-manipulated corn systems than in the weed-free plots. Trophic relationships in the weedy habitats were more complex than food webs in monocultures.Predator diversity (measured as mean number of species per area) and predator density was higher in com plots surrounded by mature, complex vegetation than at those surrounded by annual crops. This suggests that diverse adjacent areas to crops provide refuge for predators, thus acting as colonization sources.     

Soil organic carbon dynamics of croplands in European Russia: estimates from the “model of humus balance”

The Model of Humus Balance was used to estimate the influence of climate effects and changing agricultural practices on carbon (C) levels in soddy–podzolic soils in the Russian Federation for the years 2000–2050. The model was linked with a spatial database containing soil, climate and farming management layers for identification of spatial change of C sequestration potential. Analysis of relationships between C, soil texture and climate indicated that compared with a business-as-usual scenario, adaptation measures could increase the number of polygons storing soil organic carbon (SOC) by 2010–2020. The rate of possible C loss is sensitive to the different climate scenarios, with a maximum potential for SOC accumulation expected in 2030–2040, thereafter decreasing to 2050. The effect is most pronounced for the arid part of the study area under the emission scenario with the highest rate of increase in atmospheric CO₂ concentration, supporting findings from the dynamic SOC model, RothC. C sequestration during the study period was permanent for clay and clay loam soils with a C content of more than 2%, suggesting that C sequestration should be focused on highly fertile, fine-textured soils. We also show that spatial heterogeneity of soil texture can be a source of uncertainty for estimates of SOC dynamics at the regional scale. Figures in color are available at     

Importance of information on tillage practices in the modelling of environmental processes and in the use of environmental indicators

Tillage has been and will always be integral to crop production. Tillage can result in the degradation of soil, water, and air quality. Of all farm management practices, tillage may have the greatest impact on the environment. A wide variety of tillage equipment, practices and systems are available to farmers, providing opportunities to enhance environmental performance. These opportunities have made tillage a popular focus of environmental policies and programs such as environmental indicators for agriculture. This paper provides a very brief examination of the role of tillage in crop production, its effect on biophysical processes and, therefore, its impact on the environment. Models of biophysical processes are briefly examined to demonstrate the importance of tillage relative to other farm management practices and to demonstrate the detail of tillage data that these models can demand. The focus of this paper is an examination of the use of information on tillage in Canada's agri-environmental indicators initiative, National Agri-environmental Health Analysis and Reporting Program (NAHARP). Information on tillage is required for several of the indicators in NAHARP. The type of data used, its source, and its quality are discussed. Recommendations regarding the collection of tillage data and use of tillage information are presented.     

A regression tree-based method for integrating land-cover and land-use data collected at multiple scales

As data sets of multiple types and scales proliferate, it will be increasingly important to be able to flexibly combine them in ways that retain relevant information. A case in point is Amazonia, a large, data-poor region where most whole-basin data sets are limited to understanding land cover interpreted through a variety of remote sensing techniques and sensors. A growing body of work, however, indicates that the future state of much of Amazonia depends on the land use to which converted areas are put, but land use in the tropics is difficult to assess from remotely sensed data alone. An earlier paper developed new snapshots of agricultural land use in this region using a statistical fusion of satellite data and agricultural census data, an underutilized ancillary data source available across Amazonia. The creation of these land-use maps, which have the spatial detail of a satellite image and the attribute information of an agricultural census, required the development of a new statistical technique for merging data sets at different scales and of fundamentally different data types. Here we describe and assess this nonlinear technique, which reinterprets existing land cover classifications by determining what categories are most highly related to the polygon land-use data across the study area. Although developed for this region, the technique appears to hold broad promise for the systematic fusion of multiple data sets that are closely related but of different origins. The figures in the printed version of this article appear in black and white. Color figures are available from the author upon request.     

Studying crop sequences with CarrotAge,a HMM-based data mining software

We have developed a knowledge discovery system based on high-order hidden Markov models for analyzing spatio-temporal data bases. This system, named CarrotAge , takes as input an array of discrete data – the rows represent the spatial sites and the columns the time slots – and builds a partition together with its a posteriori probability. CarrotAge has been developed for studying the cropping patterns of a territory. It uses therefore an agricultural drench database, named Ter-Uti , which records every year the land-use category of a set of sites regularly spaced. The results of CarrotAge are interpreted by agronomists and used in research works linking agricultural land use and water management. Moreover, CarrotAge can be used to find out and study crop sequences in large territories, that is a main question for agricultural and environmental research, as discussed in this paper.     

Estimating emissions from crop residue open burning in China based on statistics and MODIS fire products

With the objective of reducing the large uncertainties in the estimations of emissions from crop residue open burning, an improved method for establishing emission inventories of crop residue open burning at a high spatial resolution of 0.25°× 0.25° and a temporal resolution of1 month was established based on the moderate resolution imaging spectroradiometer(MODIS) Thermal Anomalies/Fire Daily Level3 Global Product(MOD/MYD14A1). Agriculture mechanization ratios and regional crop-specific grain-to-straw ratios were introduced to improve the accuracy of related activity data. Locally observed emission factors were used to calculate the primary pollutant emissions. MODIS satellite data were modified by combining them with county-level agricultural statistical data, which reduced the influence of missing fire counts caused by their small size and cloud cover. The annual emissions of CO_2, CO, CH_4,nonmethane volatile organic compounds(NMVOCs), N_2O, NO_x, NH_3, SO_2, fine particles(PM2.5),organic carbon(OC), and black carbon(BC) were 150.40, 6.70, 0.51, 0.88, 0.01, 0.13, 0.07, 0.43,1.09, 0.34, and 0.06 Tg, respectively, in 2012. Crop residue open burning emissions displayed typical seasonal and spatial variation. The highest emission regions were the Yellow-Huai River and Yangtse-Huai River areas, and the monthly emissions were highest in June(37%).Uncertainties in the emission estimates, measured as 95% confidence intervals, range from a low of within ±126% for N_2O to a high of within ± 169% for NH_3.     

Recruitment and attrition of associated plants under a shading crop canopy: Model selection and calibration

Associated plant and animal diversity provides ecosystem services within crop production systems. The importance of the maintenance or restoration of diversity is therefore increasingly acknowledged. Here we study the population dynamics of associated annual plants (‘weeds’) during the growth of a crop in a season and introduce a minimal model to characterize the recruitment and attrition of the associated plants under the influence of shading by the crop. A mechanistically based, logistic, light interception model was parameterized with light interception measurements in two single crops (barley and rye) and in mixtures of these cereals with peas. Population dynamics data were collected for the annuals Papaver rhoeas, Centaurea cyanus, Chrysanthemum segetum, and Misopates orontium. A minimal population dynamics model was identified for each annual plant species, using system identification techniques as model selection and calibration.     

Degradation and plant uptake of nonylphenol (NP) and nonylphenol-12-ethoxylate (NP12EO) in four contrasting agricultural soils

Nonylphenol polyethoxylates (NPEOs) are surfactants found ubiquitously in the environment due to widespread industrial and domestic use. Biodegradation of NPEOs produces nonylphenol (NP), an endocrine disruptor. Sewage sludge application introduces NPEOs and NP into soils, potentially leading to accumulation in soils and crops. We examined degradation of NP and nonylphenol-12-ethoxylate (NP12EO) in four soils. NP12EO degraded rapidly (initial half time 0.3-5 days). Concentrations became undetectable within 70-90 days, with a small increase in NP concentrations after 30 days. NP initially degraded quickly (mean half time 11.5 days), but in three soils a recalcitrant fraction of 26-35% remained: the non-degrading fraction may consist of branched isomers, resistant to biodegradation. Uptake of NP by bean plants was also examined. Mean bioconcentration factors for shoots and seeds were 0.71 and 0.58, respectively. Removal of NP from the soil by plant uptake was negligible (0.01-0.02% of initial NP). Root concentrations were substantially higher than shoot and seed concentrations.     

镍的作物效应及临界值研究

在三种紫色土（酸性，中性，石灰性）上添加Ｎｉ（０，１０，３０，６０，１００，２００ｍｇ／ｋｇ土）进行莴笋盆栽试验，结果表明：低浓度的Ｎｉ能刺激作物生长，而高浓度的Ｎｉ则阻碍作物生长，使叶片黄化。三种土因性质及本身肥力水平的差异，使酸性紫色土上作物Ｎｉ最敏感，石灰性紫色土上次之，中性紫色土上最不敏感，且Ｎｉ在作物体内分配规律也有所不同，比较了土壤全Ｎｉ，有效Ｎｉ，植株根Ｎｉ作为的临界值的优越性，结果