Left to right: Justin Baker, Arturo Saldivar, Edward Castaneda. Pulling core to determine standing crop (fine root biomass) in mangrove forest at SRS-4, Shark River Slough

This material is based upon work supported by the National Science Foundation through the Florida Coastal Everglades Long-Term Ecological Research program under Cooperative Agreements #DBI-0620409 and #DEB-9910514. This image is made available for non-commercial or educational use only.

Gender Differences in Early Reading Strategies: a Comparison of Synthetic Phonics Only with a Mixed Approach to Teaching Reading to 4-5 Year-Old Children

A survey of primary schools in England found that girls outperform boys in English across all phases (Ofsted in Moving English forward. Ofsted, Manchester, 2012). The gender gap remains an on-going issue in England, especially for reading attainment. This paper presents evidence of gender differences in learning to read that emerged during the development of a reading scheme for 4- and 5-year-old children in which 372 children from Reception classes in sixteen schools participated in 12-month trials. There were three arms per trial: Intervention non-PD (non-phonically decodable text with mixed methods teaching); Intervention PD (phonically decodable text with mixed methods teaching); and a ‘business as usual’ control condition SP (synthetic phonics and decodable text). Assignment to Intervention condition was randomised. Standardised measures of word reading and comprehension were used. The research provides statistically significant evidence suggesting that boys learn more easily using a mix of whole-word and synthetic phonics approaches. In addition, the evidence indicates that boys learn to read more easily using the natural-style language of ‘real’ books including vocabulary which goes beyond their assumed decoding ability. At post-test, boys using the nonphonically decodable text with mixed methods (Intervention A) were 8 months ahead in reading comprehension compared to boys using a wholly synthetic phonics approach.

A review of weed management in wheat using crop competition

Wheat occupies a principal place in the diet of humans globally, contributing more to our daily calorie and protein intake than any other crop. For this reason, preventing weed induced yield losses in wheat has high significance for world food sustainability. Herbicides and tillage play an important role in weed control, but their use has often unacceptable consequences for humans and the wider environment. Additionally, the range of herbicides effective on key weeds is dwindling due to the evolution of herbicide resistance. Elevating crop competitiveness against weeds, through a combination of wheat breeding and innovative planting design (planting density, row spacing and orientation), has strong potential to reduce weed-induced yield losses in wheat. The last decade of research has provided a solid foundation for the breeding of weed suppressive wheat cultivars, and continued research in this area should be a focus for the future. In the interim, there is cause for optimism that weeds can be effectively suppressed using existing wheat varieties, through careful cultivar selection and choice of planting design. Further research is required to define the nature of relationships between cultivar traits and competitive planting strategies, across diverse weed flora in multiple countries, sites and seasons. Investment in such innovation promises to produce benefits, not only in terms of sustained wheat yields, but also in terms of human and ecosystem health, through ameliorating chemical and sediment contamination, soil degradation, and CO2 pollution.

Crop Insurance For Maryland Field Crops And Livestock

Multi-peril crop insurance is a valuable risk management tool which allows you to insure against losses on your farm due to adverse weather conditions, price fluctuations, and unavoidable pests and diseases. It shifts unavoidable production risks to an insurance company for the payment of a fixed amount of premium per acre. This publication assists readers in understanding the basics of the federal crop insurance program.

Dynamic price dispersion in Bertrand–Edgeworth competition

This paper studies a dynamic oligopoly model of price competition under demand uncertainty. Sellers are endowed with one unit of the good and compete by posting prices in every period. Buyers each demand one unit of the good and have a common reservation price. They have full information regarding the prices posted by each firm in the market; hence, search is costless. The number of buyers coming to the market in each period is random. Demand uncertainty is said to be high if there are at least two non-zero demand states that give a seller different option values of waiting to sell. Our model features a unique symmetric Markov perfect equilibrium in which price dispersion prevails if and only if the degree of demand uncertainty is high. Several testable theoretical implications on the distribution of market prices are derived.

A risk worth taking: perceived risk as moderator of satisfaction, loyalty, and willingness-to-pay premium price

This research investigates the moderating role of perceived risk in the relationship between satisfaction, loyalty, and willingness to pay premium price (WTP). A total of 364 respondents were recruited from Amazon's Mechanical Turk panel. Financial, social, performance, and psychological risks have significant negative effects on the relationship between satisfaction and WTP, whereas only financial risks have significant negative effects on the relationship between loyalty and WTP. Using moderated mediation analysis, we found that the mediating effects of loyalty diminish significantly in high social risk conditions and diminish completely in high financial risk conditions.

Dairy cattle-slurry treatment technologies: towards an integrating approach of crop production, animal feeding and environmental impacts

Dairy cattle farms have a well-known environmental impact that affects all ecological compartments: air, soil, water and biosphere [1]. Dairy cattle farming are a significant source of anthropogenic gases from enteric fermentation, manure storage and land application, mainly ammonia (NH3), nitric oxide (NO), nitrous oxide (N2O), carbon dioxide (CO2) and methane (CH4). The emission of such gases represents not only an environmental problem but also leads to energy and nitrogen (N) losses in ruminant production systems [2-5]. Several efforts are required on the development of new technologies and strategies that mitigate gaseous emissions, N losses and improve the efficiency of the energy and N cycles [6, 7]. In the Northwest of Portugal, dairy cattle production has a major impact on the economy, with strong repercussions at national scale. Therefore, our Ph.D. thesis project aims to: a) Study natural supplements as additives in the dairy cattle diet towards a decrease in GHG emissions from feeding operations; b) Compare commercial dairy cattle diets with and without additives on gaseous emissions from manure deposited in a simulated concrete floor; c) Assess the concentrations and emissions of NH3 and greenhouse gases from commercial dairy cattle facilities; d) Evaluate the effects of different additives on lowering gaseous emissions from dairy cattle excreta, using a laboratory system simulating a dairy house concrete floor.

Mobilizing Greater Crop and Land Potentials Sustainably

The supply side of the food security engine is the way we farm. The current engine of conventional tillage farming is faltering and needs to be replaced. This presentation will address supply side issues of agriculture to meet future agricultural demands for food and industry using the alternate no-till Conservation Agriculture (CA) paradigm (involving no-till farming with mulch soil cover and diversified cropping) that is able to raise productivity sustainably and efficiently, reduce inputs, regenerate degraded land, minimise soil erosion, and harness the flow of ecosystem services. CA is an ecosystems approach to farming capable of enhancing not only the economic and environmental performance of crop production and land management, but also promotes a mindset change for producing ‘more from less’, the key attitude towards sustainable production intensification. CA is now spreading globally in all continents at an annual rate of 10 Mha and covers some 157 Mha of cropland. Today global agriculture produces enough food to feed three times the current population of 7.21 billion. In 1976, when the world population was 4.15 billion, world food production far exceeded the amount necessary to feed that population. However, our urban and industrialised lifestyle leads to wastage of food of some 30%-40%, as well as waste of enormous amount of energy and protein while transforming crop-based food into animal-derived food; we have a higher proportion of people than ever before who are obese; we continue to degrade our ecosystems including much of our agricultural land of which some 400 Mha is reported to be abandoned due to severe soil and land degradation; and yields of staple cereals appear to have stagnated. These are signs of unsustainability at the structural level in the society, and it is at the structural level, for both supply side and demand side, that we need transformed mind sets about production, consumption and distribution. CA not only provides the possibility of increased crop yields for the low input smallholder farmer, it also provides a pro-poor rural and agricultural development model to support agricultural intensification in an affordable manner. For the high output farmer, it offers greater efficiency (productivity) and profit, resilience and stewardship. For farming anywhere, it addresses the root causes of agricultural land degradation, sub-optimal ecological crop and land potentials or yield ceilings, and poor crop phenotypic expressions or yield gaps. As national economies expand and diversify, more people become integrated into the economy and are able to access food. However, for those whose livelihoods continue to depend on agriculture to feed themselves and the rest of the world population, the challenge is for agriculture to produce the needed food and raw material for industry with minimum harm to the environment and the society, and to produce it with maximum efficiency and resilience against abiotic and biotic stresses, including those arising from climate change. There is growing empirical and scientific evidence worldwide that the future global supplies of food and agricultural raw materials can be assured sustainably at much lower environmental and economic cost by shifting away from conventional tillage-based food and agriculture systems to no-till CA-based food and agriculture systems. To achieve this goal will require effective national and global policy and institutional support (including research and education).

Degradation and sorption of fipronil and atrazine in Latossols with organic residues from sugarcane crop.

ABSTRACT: Organic residues from sugarcane crop and processing (vinasse, boiler ash, cake filter, and straw) are commonly applied or left on the soil to enhance its fertility. However, they can influence pesticide degradation and sorption. The objective of this study was to assess the effect of adding these organic residues on the degradation and sorption of fipronil and atrazine in two soils of the State of Mato Grosso do Sul, MS, Brazil. The degradation experiment was carried out with laboratory-incubated (40 days; 28°C; 70% field capacity) soils (0-10cm). The batch equilibration method was used to determine sorption. Fipronil (half-life values of 15-105 days) showed to be more persistent than atrazine (7-17 days). Vinasse application to the soil favored fipronil and atrazine degradation, whereas cake filter application decreased the degradation rates for both pesticides. Values for sorption coefficients (Kd) were determined for fipronil (5.1-13.2mL g-1) and atrazine (0.5-1.5mL g-1). Only straw and cake filter residues enhanced fipronil sorption when added to the soil, whereas all sugarcane residues increased atrazine sorption. RESUMO: Resíduos orgânicos do cultivo e processamento da cana-de-açúcar (vinhaça, cinzas, torta de filtro e palha) são usualmente aplicados ou deixados no solo para aumentar sua fertilidade, mas eles podem influenciar na degradação e sorção de agrotóxicos. O objetivo deste estudo foi avaliar o efeito da adição desses resíduos orgânicos no solo sobre a degradação e sorção do fipronil e da atrazina em dois solos no Estado de Mato Grosso do Sul, MS, Brasil. O experimento de degradação foi realizado com solos (0-10cm) incubados em laboratório (40 dias; 28°C; 70% da capacidade de campo). Para determinar a sorção, foi usado o método da batelada. Fipronil mostrou ser mais persistente (valores de meia-vida entre 15-105 dias) que atrazina (7-17 dias). O solo com adição de vinhaça favoreceu a degradação de fipronil e atrazina, enquanto adição da torta de filtro desacelerou o processo. Os valores do coeficiente de sorção (Kd) foram determinados para fipronil (5,1-13,2mL g-1) e atrazina (0,5-1,5mL g-1). Apenas os resíduos palha e torta de filtro aumentaram a sorção de fipronil quando adicionados ao solo, enquanto todos os resíduos aumentaram a sorção de atrazina.

Comparison of multitemporal MODIS-EVI smoothing algorithms and its contribution to crop monitoring.

Crop monitoring and more generally land use change detection are of primary importance in order to analyze spatio-temporal dynamics and its impacts on environment. This aspect is especially true in such a region as the State of Mato Grosso (south of the Brazilian Amazon Basin) which hosts an intensive pioneer front. Deforestation in this region as often been explained by soybean expansion in the last three decades. Remote sensing techniques may now represent an efficient and objective manner to quantify how crops expansion really represents a factor of deforestation through crop mapping studies. Due to the special characteristics of the soybean productions' farms in Mato Grosso (area varying between 1000 hectares and 40000 hectares and individual fields often bigger than 100 hectares), the Moderate Resolution Imaging Spectroradiometer (MODIS) data with a near daily temporal resolution and 250 m spatial resolution can be considered as adequate resources to crop mapping. Especially, multitemporal vegetation indices (VI) studies have been currently used to realize this task [1] [2]. In this study, 16-days compositions of EVI (MODQ13 product) data are used. However, although these data are already processed, multitemporal VI profiles still remain noisy due to cloudiness (which is extremely frequent in a tropical region such as south Amazon Basin), sensor problems, errors in atmospheric corrections or BRDF effect. Thus, many works tried to develop algorithms that could smooth the multitemporal VI profiles in order to improve further classification. The goal of this study is to compare and test different smoothing algorithms in order to select the one which satisfies better to the demand which is classifying crop classes. Those classes correspond to 6 different agricultural managements observed in Mato Grosso through an intensive field work which resulted in mapping more than 1000 individual fields. The agricultural managements above mentioned are based on combination of soy, cotton, corn, millet and sorghum crops sowed in single or double crop systems. Due to the difficulty in separating certain classes because of too similar agricultural calendars, the classification will be reduced to 3 classes : Cotton (single crop), Soy and cotton (double crop), soy (single or double crop with corn, millet or sorghum). The classification will use training data obtained in the 2005-2006 harvest and then be tested on the 2006-2007 harvest. In a first step, four smoothing techniques are presented and criticized. Those techniques are Best Index Slope Extraction (BISE) [3], Mean Value Iteration (MVI) [4], Weighted Least Squares (WLS) [5] and Savitzky-Golay Filter (SG) [6] [7]. These techniques are then implemented and visually compared on a few individual pixels so that it allows doing a first selection between the five studied techniques. The WLS and SG techniques are selected according to criteria proposed by [8]. Those criteria are: ability in eliminating frequent noises, conserving the upper values of the VI profiles and keeping the temporality of the profiles. Those selected algorithms are then programmed and applied to the MODIS/TERRA EVI data (16-days composition periods). Tests of separability are realized based on the Jeffries-Matusita distance in order to see if the algorithms managed in improving the potential of differentiation between the classes. Those tests are realized on the overall profile (comprising 23 MODIS images) as well as on each MODIS sub-period of the profile [1]. This last test is a double interest process because it allows comparing the smoothing techniques and also enables to select a set of images which carries more information on the separability between the classes. Those selected dates can then be used to realize a supervised classification. Here three different classifiers are tested to evaluate if the smoothing techniques as a particular effect on the classification depending on the classifiers used. Those classifiers are Maximum Likelihood classifier, Spectral Angle Mapper (SAM) classifier and CHAID Improved Decision tree. It appears through the separability tests on the overall process that the smoothed profiles don't improve efficiently the potential of discrimination between classes when compared with the original data. However, the same tests realized on the MODIS sub-periods show better results obtained with the smoothed algorithms. The results of the classification confirm this first analyze. The Kappa coefficients are always better with the smoothing techniques and the results obtained with the WLS and SG smoothed profiles are nearly equal. However, the results are different depending on the classifier used. The impact of the smoothing algorithms is much better while using the decision tree model. Indeed, it allows a gain of 0.1 in the Kappa coefficient. While using the Maximum Likelihood end SAM models, the gain remains positive but is much lower (Kappa improved of 0.02 only). Thus, this work's aim is to prove the utility in smoothing the VI profiles in order to improve the final results. However, the choice of the smoothing algorithm has to be made considering the original data used and the classifier models used. In that case the Savitzky-Golay filter gave the better results.