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The objective of this paper is to introduce a new model, Pearl, designed to evaluate bay sanitation conditions and manage shellfish growing area closures. The Pearl model can be used in one of two modes. In the stand-alone mode, Pearl can perform a multi-year analysis using observed fecal coliform data collected from within shellfish growing areas to determine if shellfish harvested from those areas may pose a human health risk for shellfish consumers. Shellfish growing areas that are identified through a stand-alone Pearl analysis are candidates for closure rule adjustments. Run in tandem mode with a previously released model, Aquarius, Pearl can be used to adjust closure rules and maximize the number of days a shellfish growing area can remain open to harvest with no increased risk of illness to shellfish consumers. Pearl increases the sensitivity of the existing National Shellfish Sanitation Program's closure assessment method. The use of Pearl could open harvesting areas that might otherwise be closed, thereby increasing the profits of shellfish growers with no increased risk of illness to shellfish consumers.
Construction of the Itaparica dam and reservoir induced changes of the agricultural production systems in the micro-region Itaparica, at the lower-middle São Francisco river basin. Extensive traditional systems were replaced by irrigated fruit and production. Over twenty years after the dam construction, many farmers are still facing income insecurity. A survey consisting of expert interviews and structured on-farm interviews was conducted to analyze current production systems. Farm income depended strongly on low wages for day laborers, free irrigation water, and stable prices of the main crop, coconut. Diversification of production and improved market access can help to improve farmers’ income situation.
Increased demand of farm productions and depleting natural resources compelled the agriculturecommunity to enhance the use of Information and Communication Technology (ICT) in variousfarming processes. Agricultural Decision Support System (DSS) proved useful in this regard asthe agricultural systems are complex and partially known. The majority of available AgriculturalDSSs are either crop or task specific. There are very less endeavors found in the direction ofcomprehensive DSS. The specific DSSs mainly developed with rule based or knowledge transferbased approach. These methodologies lack the ability to scale up and to support the developmentof large DSS. Modeling approaches are more suitable than so called transfer approaches for largeand inclusive DSS. Unfortunately, it is found that that the model based knowledge engineeringapproach is not much utilized for the development of Agricultural DSS. The modeling approachto construct Knowledge Base Systems (KBS) becomes well accepted among the KnowledgeEngineering (KE) communities due to its modular structure and ability to break down theknowledge engineering problem into smaller tasks. Modeling approach for the development ofDSS offers the broad idea of structure and modules of the support system before hand. There aremany modeling frameworks proposed and subsequently used by the KE communities.CommonKADS is one of the popular modeling frameworks for KBS. The paper presents theorganization, agent, task, communication, knowledge and design models based onCommonKADS approach for development of scalable, broad and practically usable agriculturalDSS. A web based DSS developed with multi agent CommonKADS modeling approach. Thesystem offers decision support for irrigation scheduling and weather based disease forecastingfor the popular crops of India. The proposed framework along with the required expertknowledge, provide necessary platform on which the larger DSS can built for any crop of givenlocations.
The rapid growth of mobile usage in the world has created many opportunities to leverage the ubiquity of mobile network. Mobile applications have gained momentum in many sectors including agriculture. However, development of mobile apps is still facing many challenges, including platform fragmentation and rapid changes of the mobile technology. It hence has a great benefit to identify common frameworks and solutions that allow developers to leverage a single codebase and deploy it on multiple platforms, both on different mobile devices and mobile web. This paper explores current mobile technologies and design options of mobile databases to develop cross-platform mobile apps. Discussions are made using the proposed framework for a mobile project named Southeast Landscape Pests developed at University of Florida/IFAS. The project leverages cross-platform technologies and JSON as an alternative for database storage. The solution is a cost effective approach to deploy apps to iOS, Android and Web and could be adopted for other small size mobile projects.
Steering agricultural machinery during the harvest in fields is a tedious task for drivers. Automated guidance of the machinery will not only reduce driver fatigue but also increase productivity and safety of the harvesting operation. The existing mechanical solutions for row navigation on the sugar beet fields do not provide sufficient accuracy during the harvesting process. The next important factor in optimizing equipment performance and marketable yield is depth control or distance control. Gauge wheels or rollers are common mechanical devices used to reach the correct depth or distance. These systems have limited versatility requiring the operator to make frequent on-the-go adjustments. The objectives of the research presented in this paper were to develop and optimize a contactless sensor system for row navigation and automatic depth control for a sugar beet harvester.
During the last years new technologies occurred - some will help agriculture a lot. Main tasks to make them profitable for sustainable resource management is technology assessment based on detailed know how of a country’s situation and needs, further technology integration concepts including an ICT infrastructure that integrates next to Ministries or public bodies also large private entities within agriculture and forestry. Ortho-images or networks of weather-stations are part of the concept and must be access-able by WMS (Web Map Services) from all parties, if wished against an access fee. Beside technology and the ICT backbone one has to understand all stakeholders and correlations of the information flow within the agro-(forest-, environment- natural-risk-) -processes with all tasks. Business-models have to be set up integrating different stakeholders allowing IT-supported cooperation. Benefits must be identified and valuated to know financial commitment(s), guidance is recommended by local and international experts of all sectors.
The University of Debrecen in Hungary has already 30 years’ experience in computing,informatics training in agriculture. The education programs in agri-informatics have beencontinuously changed in various accredited training programs. The different levels of ITknowledge and education programs / subjects exist in different levels, such as VocationalEducation, BSc, MSc and PhD levels. The five years long Informatics Agricultural Engineereducation program has been introduced in the 2002/2003 academic year. During the past decadethe reform of higher education was started to implement according to the Bologna declaration.The new Hungarian Higher Education system (the Bologna system) was introduced in the2006/2007 academic year. From this date two-tier (separate bachelor-and-master-level) programswere introduced on the 3 + 2 model in certain disciplines. The agro-informatics BSc in 2006 anda new MSc education program in 2013 have been accredited. The enterprises and farmers in theHungarian agro-food sector and governmental offices demanded to start this course becausethere is digital gap between the sector of agro-food and other sectors, especially between ruraland non-rural areas. This statement is justified by numbers of our researches and PhD thesis. TheBSc course gives practice-oriented knowledge and competences, the MSc gives deeplytheoretical ones. The rate of the core informatics and applied informatics subjects is about 40 %of the subjects groups (agronomy, informatics, economics and management, law and publicadministration). This paper gives information about the development roadmap, results, demands,the accreditation process, the content of the new MSc agri-informatics programs and thechallenges.
The aim of the AgroFE project is to play an important role in Agro-forestry trainings. Depending on the European countries, states or professional organizations and training actors try to reintroduce Agro forestry in the course of training and qualification in initial training and in adult education. The main objectives are to make a synthesis of needs and expectations, based on present the existing training actions and to set up a common framework; to build an innovative training system (contextualized, modularized trainings, use of ICT, professionals participation); to create a technical collaborative support for the implementation of the project with communication tools (information of partners and promotion) and for providing access to the resources and training services during and after the project (knowledge databank, interactive services). The system architecture is based on open source concept. Open source subsystems are the Search Engine (harvester), Digital Asses Management System (KDB), Content Management System for internet portal, Editorial Chain (workflow) Management, E-Learning platform. The user profiles (user groups) are Teachers, Professionals, Students, People with disabilities in professional situations, Knowledge feeders, Knowledge builders and aggregators. There are needs for handling many formats in the knowledge databank (Text in different file structures; Pictures or images; Audio and video files; Data and figures; Container format for exchange contents Pdf, html, Epub 3, etc. The Moodle system and videoconference servers (which can be used by videoconference equipment, user client software on desktop, notebook and smart-phone) is used for Collaborative working and learning environment. The Moodle (powerful, secure open source) learning and collaborative platform is used as project management and project assessments tools.
It is well known that weeds compete with the cultivated plant for the nutrient and water, thus weed coverage could a great influence on the profitability of production. Accordingly, to define the distribution of weed patches is a very important task in precision agriculture. Some traditional and modern methods are available to scout weeds. To survey weed coverage on larger fields by traditional methods is often time consuming. Remote sensing instruments are effective tools to detect weeds in larger area and it is relatively cheaper than traditional way. Two new different techniques of the remote sensing technologies become known: the airborne laser scanning (ALS) and the hyperspectral imaging. From the hyperspectral data, vegetation indices can be calculated, which can help to segment weeds form the soil. In this paper, the writers present a method, where the airborne LIDAR and hyperspectral technique were integrated into a common geoinformatics environment. The test site was a one hectare arable field. Different surface coverage categories were defined by the hyperspectral image. Based on these classes, an n-dimensional classification algorithms were used to define the combination of weed coverage and the extent of biomass. Based on the laser scanning data set a 3D surface was created with runoff conditions. Determination of weed coverage is very important, while it could provide basic information for managing to pass the herbicides out in precision agriculture.
Agroforestry systems are part of the history of the European Union rural landscapes, but the regional increase of size of agricultural parcels had a significant effect on European landuse in the 20th century, thereby it has radically reduced the coverage of natural forest. However, this cause conflicts between interest of agricultural and forestry sectors. The agroforestry land uses could be a solution of this conflict management. One real – ecological - problem with these forests is the partly missing of network function without connecting ecological green corridors, the other problem is verifiability for the agroforestry payment system, monitoring the arable lands and plantations.Remote sensing methods are currently used to supervise European Union payments. Nowadays, next to use satellite imagery the airborne hyperspactral and LiDAR (Light Detection And Ranging) remote sensing technologies are becoming more widespread use for nature, environmental, forest, agriculture protection, conservation and monitoring and it is an effective tool for monitoring biomass production.In this Hungarian case study we made a Spatial Decision Support System (SDSS) to create agroforestry site selection model. The aim of model building was to ensure the continuity of ecological green corridors, maintain the appropriate landuse of regional endowments. The investigation tool was the more widely used hyperspectral and airborne LiDAR remote sensing technologies which can provide appropriate data acquisition and data processing tools to build a decision support system.
Most of the countries in the Asian monsoon region are agricultural areas, and the impact of climate change on this region is very significant. Rainfed areas in particular are very vulnerable to extreme weather events such as floods and droughts. Moreover, the chage of the onset and period of the rainy season causes serious effects on yields and farming plans. We are developing an evaluation system for major crops in the Asian monsoon region affected by climate changes that can simulate the growth of major crops in this region. We selected Thailand as a target country. and DSSAT as a crop model. In 2012, we developed a prediction system to optimize the double cropping of rice and cassava in northeastern Thailand. We developed this system to identify and recommend bonus crops that can be grown during a dry season, and to simulate the effects of climate change in northeastern Thailand’s rainfed farming areas. In 2014 to simulate the effects of climate changes in the mountainous area of Thailand, we expanded the target area of the system to the northern mountain area, and we increased the number of crops in the model. The system executes crop model 365 times, while the start date is moved by one day each time, to identify the best start date using meteorological data that take climate change into consideration. The best start date and yield are summarized and shown on a map of the area being studied. The system is flexible and can be applied to other areas, as users are able to select crop models such as SIMRIW and ORYZA2000. The current areas of concern for the model include the execution time of several days needed to simulate tens of thousands of grid points and handling of the huge number of files output by the crop model. We are trying to apply multi-thread, Hadoop, and other devices to address these issues.
IICA recognizes that information management and knowledge sharing are fundamental for improving agriculture and rural life in the countries of Latin America and the Caribbean (LAC). Both information and knowledge are essential in decision making, research and education, as well as in achieving competitiveness, innovation and sustainable development. SIDALC is an international alliance of information services in which institutions from 22 countries in the Americas share their agricultural, livestock, forestry and environmental information. Created in 1999, today SIDALC is one of the most important sources of knowledge and information for agriculture in LAC both on the web and offline. SIDALC’s success lies in the fact that it has taken advantage of the intellectual capital of 172 national institutions and national networks that, acting as intermediaries in the management and dissemination of information, continually expand and modernize their services in light of the new paradigms of the “knowledge society”. Because of this, SIDALC is the largest community of information specialists interested in providing services openly around the globe. They collaborate with one another, “sharing a little so that all can have more,” while conserving their identity and policies. SIDALC is open to the public, at www.sidalc.net (no subscription required), permitting on-line searches of 2.6 millions of records and available digital collections including more than 225.013 full-text documents. Furthermore, SIDALC is linked to other information systems and networks in a number of countries of the Americas, which provides stakeholders in agriculture and rural territories with a direct response to their information needs from reference desks around LAC.
One issue that has occupied the attention of humanity is the production of food and view it from several perspectives: the quality of the seed, the production process, diseases that affect productivity, the effect of climate and location, in example. As a contribution to the above situation, this paper presents the application of one discipline of artificial intelligence, known as machine learning, which involves the study of computer algorithms that improve automatically through experience. This type of learning has been used in applications ranging from data mining to discover rules in large datasets, to information filtering systems that automatically learn user interests. As a particular case, the Corporación Bananera Nacional of Costa Rica (Corbana) has stations measuring meteorological variables. These variables measured are temperature, precipitation, humidity, wind speed, among others. Corbana is interested in relating this information with the spread of a disease that affects production; this disease is the sigatoka. In addition, this organization recorded weekly in several of his research areas the following variables related to that disease: state of evolution, severity leaf 2, severity leaf 3, among others. With this data and using machine learning algorithms; they want to make predictions. This work presents the results of applying various machine learning algorithms to available data, in example, artificial neural network, support vector machines regression. These first conclusions will be tuned in the future.
Since the beginning of Extension a century ago, Extension faculty have used the latest technology and research to meet clientele needs. Today, Extension Faculty have numerous tools at their fingertips to help them reach new audiences and save time. In fact, there are so many tools and the technology develops so rapidly that many faculty have a hard time identifying the best tools to meet their clientele needs and the Extension budget to deliver educational programming. To use technology successfully to deliver high-quality programming, Extension faculty need to be able to:
This paper makes a case for harnessing indigenous knowledge (IK) for sustainable national development in Ghana. IK is the local knowledge that is unique to a given culture or society. It is the basis for local-level decision-making in agriculture, health care, food preparation, education, natural resource management, and a host of other activities in rural communities. According to the World Bank the basic component of any country‟s knowledge system is its indigenous knowledge. It is upon this knowledge that scientific research builds. In Ghana the Government has recognized the need to harness IK for sustainable national development and has therefore incorporated it into the National Science, Technology and Innovation Development Programme. There is no evidence however that scientific research in Ghana bases its activities on IK. The paper discusses the need to take IK seriously in Ghana and establishes its importance as a foundation for scientific research. It discusses the concept of indigenous knowledge in the forestry sector, its relevance in scientific discourse and the need for harnessing IK for national development. It further goes on to give some examples of IK application in the forestry sector of Ghana and emphasizes its importance in forest management. In harnessing indigenous knowledge, several issues need to be considered. For example who owns the local knowledge, who is responsible for validating it, what factors need to be considered during the validation process and what methods can be used to record, store and retrieve the IK identified.
Innovation occurs as a result of the creativity and interplay between actors combining new and/orexisting (tacit) knowledge. As input to a report on agricultural knowledge and innovationsystems (EU SCAR, 2013), we analyzed the use of social media and other information andcommunication technologies (ICT) tools as drivers of innovation in agriculture and other sectors.We found, that there is a great potential for using existing social software tools and platforms forcommunication, interaction, knowledge sharing, preservation of information and as suchstimulate multi-actor innovation. Apart from a few exceptions, our review of social softwaresystems revealed that agriculture as a sector to some extent has adopted the general socialsoftware programs as tools for networking and knowledge sharing, but the potential to use it forcrowdsourcing and cooperation or as a supplement to face-to-face interactions has not yet beenexploited.
The terroir has been recognized as an important factor in wine quality and style, especially in European vineyards. There is currently a need for quantification of the factors that influence the definition of terroir, incorporating indexes that quantify variables such as soil, plant and climate, which has led to the definition of “Digital Terroir ". This paper proposes a methodology to develop the “digital terroir" through use of emerging technologies, as current procedures that should be use for the study and define of terroir which suffer from having replicable protocols. The study took place in Valdivieso Vineyard, Curicó, Chile, during the 2012 and 2013 seasons, under the Var. Cabernet Sauvignon, Merlot and Carmenere. The Ferari index (MULTIPLEX RESEARCH ™, FORCE- A), was used for the grapes quality quantification, which was obtained from field samples by a high density grid (20x20 m). Moreover, the soil and plant information was obtained by the use of equipment as follows, electrical conductivity (EM38), topography and exposure (RTK) and NDVI (Tetracam ADC). From the fruit quality index distribution curve (Ferari), 7 rated strata was developed by variety and year, which was used for training the respective model classification of the variables associated with the site. The classification algorithms were based on qualifying Boosting and vector machines (SVM). For model training, 75 % of the data was used and allowed the remaining 25 % to verify the calculation error (control data). The classification results were 95 %, 90 % and 80 %, of well classified area (R2>0.9 and Mean Absolute Error < 0.1) for Var.Carmenere, Cabernet Sauvignon and Merlot, respectively. Finally, the well defined grape quality area develop could be used for differential harvest and could be use for vineyard management when increase the yield it is the main goal. The described procedure and results are a keystone for the application of INDITES software presented on this work.
As a need, and in order to generate inputs for the Coffee Sector, the Costa Rica CoffeeInstitute, ICAFE, proposes and fosters a Costa Rica coffee coverage updating as a tool forplanning and development in the area of research.Therefore, it is of utmost importance to carry out a systematic approach with the six coffeeRegions to update the nationwide coffee coverage, through Geographic InformationSystems, GIS.
Guarantying the quality and genetic purity of hybrid oil palm seeds requires precision, control, traceability and constant validation of many processes; both in the field (identifying mother palms and pollen sources, pollination, identification of bunches etc.) and in the facilities where seeds are prepared (identification of batches, cleaning of seeds, germination etc.) for final selling. All these processes can be affected by human error which may have very negative effects on the final product (germinated oil palm seeds) that are offered in the international market to start new oil palm plantations. The objective of automating is to reduce human intervention as much as possible (in the form of hand written forms and reports mainly) to ensure a better control of the information, guarantee its integrity and increase productivity of the human asset.This paper describes the changes done to old methods to accomplish the said objectives during the processes to producing and handling oil palm seeds at the ASD Costa Rica facilities in Coto, Costa Rica. Changes done included the use of mobile devices to capture data in the field and at the seed processing unit, the identification with barcodes of mother palms, pollen sources, seed batches and other key elements, all to guarantee the integrity of the information and traceability, and the reduction of possible human errors.
Infrared thermography can be used to detect water-stress induced temperature changes inplants. Bacterial wilt, a destructive disease of tomato caused by Ralstonia solanacearum,induces water stress in the host, leading to wilt and plant death. Fifty plants wereinoculated with a bacterial suspension of 108 colony forming units (CFU) and fifty noninoculatedplants were maintained as healthy controls. Leaf temperature of inoculated andnon-inoculated plants was measured with two infrared sensors, a low-cost infraredthermometer, and a thermal camera. The test was performed twice. In the firstexperiment, the incidence of bacterial wilt was 62%. Leaf temperature of healthy anddiseased plants was similar for four days after inoculation. On the fifth and sixth days,leaf temperature of inoculated plants was 0,9 °C and 1,9 ºC higher, respectively, than thetemperature of healthy plants. Wilt symptoms were first observed seven days afterinoculation. In the second experiment, which coincided with cooler weather conditions(15 ºC during the day), disease incidence was 38%. Wilt symptoms were observed 10days after inoculation, but temperature differences were observed seven days afterinoculation. The use of this methodology allowed detection of differences in temperaturetwo to three days before symptoms were visible. Application of this technology mayfacilitate management decisions for bacterial wilt.
The extremely arid climate of the Tarim Basin in Northwestern China offers ideal production conditions for cotton, making the region one of the nation’s major cotton production bases. However, in the last decades the overuse of water resources for agricultural production led to severe ecological degradation and increasing competition for water among farmers. Still the majority of farmers in the region use flood irrigation to provide water to their crops. Drip irrigation under plastic mulch constitutes a new technology that generally features increased water use efficiency, however at higher production costs. The present study assesses the costs and benefits of applying drip irrigation based on a primary household dataset collected in the region. The results show, that application of drip irrigation is only beneficial in economic terms, if farmers manage to increase their yield levels at the same time. Therefore it is recommended to improve the agricultural extension service, and cover a substantial share of the additional cost for the farmer through providing subsidy for advanced irrigation technology.
Uruguay is one of the major beef and sheep meat exporters countries. Its economy is highly dependent on livestock production, and cow-calf systems (CCS) are mainly carried out by small farmers under grazing conditions and consequently under the influence of a large number of interacting variables affecting productivity and especially the economic profit of the production systems. Compared to many other countries, fat cow/calf price ratio in Uruguay is significantly higher. Under the assumption that pregnancy diagnostic defines whether a cow is retained on the herd or cull to be fattened, and that those are sold with relatively high values, the hypothesis is that calf production and cow fattening are antagonistic activities inside the system. The aim of this study was to analyze the effect of some key variables in CCS using a bioeconomic simulation model, with special emphasis on the interactions between pregnancy rate, age at first mating (AFM) and the relationship between calves and fat cow prices. The defined CCS has enough forage to fatten all cull cows and to rear heifers (15-17 % of improved pastures). Three AFM were compared; 14 months old, 26 years old and 50% of heifers at 14 months old and 50% at 26 months. In addition, two market scenarios defined by different calf/cow price ratios were assessed (1.49 vs 1.27). Results showed that increase on pregnancy has its economic counterpart reducing cull cows for fattening. In economic terms, the importance of pregnancy as an indicator of the farm success should be relativized, especially when the fattening system component is assisted by a favorable prices relationship and an efficient process of females replacement in the herd (early AFM). Simulation models are a useful tool to identify critical points and inefficiencies in Uruguayan’s livestock systems, and to feedback research when looking for alternatives to overcome these limitations.
This study aims to analyze the phenomenon of the digital divide in the community of Tierra Blanca and present the case of the Agricultural Information Service of Tierra Blanca as a solution to reduce the digital divide in this farming community. Based on the premise that new information technologies have a great importance in today´s society, the article analyzes the concept of digital divide composed by three components: access to information, connectivity and digital literacy, contextualized in farming communities. Next, the investigation studies the relationship between the digital divide and the productive, political, economic, social, cultural and commercial business development of small farmers. Then, based on the collaboration of a group of farmers in Tierra Blanca, the impact of the digital divide is considered specifically in this community. Finally, the case of the Agricultural Information Service of Tierra Blanca is presented as an automated information system tha t allows combat lags generated by this phenomenon. We conclude that the case of the Agricultural Information Service of Tierra Blanca is an initiative that has reduced the digital divide in a comprehensive way for farmers, but the development of comprehensive policies and strategies at the community and national levels is necessary, seeking the involvement of various institutions and communities
The use of modern technologies in agriculture, such as sensors or farm management systems, generates a growing number of data on farms worldwide. These technologies as well as the mentioned big data from it, have high potentials for several scopes of applications (e.g. consulting, communication, marketing). With this background in consideration, this study aims on a screening, classification and evaluation of IT-Technologies in Agriculture - focussed on seven technologies (Application, Web-Based technology, Sensor, Satellite, Hardware, Software, and SMS) with farms as reference area. The classification of the solutions based on 31 criteria (i.g. archetype or provider type) and subgroups. At the end of our research a total number of 1684 entries were counted; most of them Applications or Software solutions. The paper will focus on selected results presenting potentials for several scopes of applications on the global market of IT-Technologies for farm management.
This study presents an application to simulate the internal temperature and relative humidity of a greenhouse, which provides a simple way to predict crop environmental requirements. Programming was developed in a worksheet with subroutines based on a steady model of energy and mass transfer and as well as psychometric relationships. It contains a user friendly interface that helps increase the analysis and design capabilities on greenhouse environments in countries with low-tech possibilities. This tool can help professionals involved in the greenhouse industry to better understand the complexity and behavior of a greenhouse environment. It can be used as a support aid to evaluate the crop´s specific sustainability in a greenhouse given climate conditions and it can also help design the mechanical systems in order to improve crop productivity. Additionally, in existing projects, this tool can be used to evaluate mechanical systems and a variety of devices that could provide optimal conditions for a crop improving investment efficiency. Implementing this method demonstrated in several validation studies carried out in Costa Rica, that it is possible to achieve suitable precision in the simulation and design of greenhouse environments using basic computational resources.
Near-Infrared spectroscopy (NIR) is a fast and non-destructive method to identify orange growingareas. In this paper, a principal component analysis (PCA) approach was used to obtain the featuresof orange NIR spectra by reducing the divisions in the analysis. An artificial neural network (ANN)was developed to achieve enhanced classification accuracy, while a support vector machine (SVM)model was proposed for higher classification accuracy. A hybrid genetic algorithm (GA) SVMmodel was designed, with the most valuable data from the PCA selecting by GA. The simulationresults showed that the hybrid GA-SVM classifier achieved the best accuracy of 89.717%.
FarmTracking® aims to provide the farmer with a unique opportunity for receiving practiceaimedinformation and decision support delivered at the right place at the right time.FarmTracking is a concept and a system that utilizes the capabilities of the smartphone incombination with central databases and GIS based to provide the farmer with context specificnotifications and alerts. A generic framework able to fulfill a number of requirements has beendeveloped, and this framework has shown its value through working prototypes for handlinglogbook for manure storage tanks and for retrieving field specific information.
There is an increasing need to promote more environmentally sustainable agriculture and small-scaled farm business in the NCP. This paper adopts two key environmental indicators to improve scientific understanding of environmental impact of summer maize and winter wheat production system and explain distinct performance results from farmer’s management aspect. This paper highlights farm management diverseness and its impact on environmental performance of crop production. The farm households are grouped into three categories based on similarity on environmental impact results. The cluster analysis shows that input intensification often results in poor performance environmental damage. To realize shift from high input intensive agriculture to sustainable agriculture, multi-scale and system approaches with sufficient reference to local specificities needs to be implemented to achieve more environmental sustainable agriculture in the NCP.
Jobos Bay National Estuarine Research Reserve (JBNERR) is located at the south eastern coast of Puerto Rico. The reserve includes subtropical forests, mangrove forests, coral reefs, seagrass beds, salt and mud flats, lagoons and freshwater wetlands. Mar Negro wetland is the biggest mangrove forest in the area, located at the south boundary of JBNERR. The north side of the Reserve has been used for agricultural activities since the Spanish Colonial times. Since 1993 the mangrove population started to diminish. Its mortality was promoted by antropogenic activities such as deforestation, domestic sewage discharging into the mangrove lagoon, change of hydrologic patterns caused by urban developments and agricultural practices. During the decade of 1990 intensive agricultural activities north of the reserve caused a negative impact on mangroves. Pesticides, fertilizers and chicken manure applied in agricultural fields were being discharged directly into de mangrove forest, mainly through two drainage earth channels. The objectives of this project were: 1) To model the hydrologic conditions existing north of JBNERR, 2) Hydraulic design of side-weirs along channels to promote uniform distribution of irrigation water from agricultural activities and runoff. Water discharges into a vegetative strip before discharging into Mar Negro. The hydraulic analysis was performed using ArcGIS, in-house computer models and, EPA’s Storm Water Management Model (SWMM). The hydraulic design resulted in an innovative system of lateral weirs which distribute water uniformly along the vegetative strip. This water distribution system controls surface runoff from irrigation and rainfall, reduces surface erosion and improves the quality of overland flows discharging into Jobos Bay.
The Arenal-Tempisque irrigation district is located in the lower part of the Tempisque and Bebedero basins. It is the major irrigation project of Costa Rica (59960 ha) and is divided into eight sub-districts. It has extensive agricultural activity (sugarcane, rice, watermelon and tilapia) with a high water consumption. Systematization of information is a priority issue to increase water distribution efficiency and enhance decision making during the dry season. An example has been developed in Cañas sub-district to analyze various hydro-geospatial variables related to water demand in order to determine the water status of the region for January, February and March, corresponding to the driest months in Guanacaste. Using GIS and remote sensing techniques, it was possible to obtain distributed water balance and water demand on farms, taking into account precipitation, temperature, evapotranspiration and runoff. Normalized difference vegetation index (NDVI) was calculated using information extracted from LANDSAT 8 satellite images available for the study area. These were used to evaluate kc and evapotranspiration to a resolution of 30 meters. Distributed runoff was computed with the SCS method. The curve number was obtained from impermeable, vegetation and soil proportions using LandSat images and the lineal spectrum unmixing technique for the separation. Results showed a total water deficit of 416.53 L/m2 with a standard deviation (SD) of 157.64 L/m2 during the 3 dry months.
The Costa Rican banana industry is mainly located in the Caribbean basin, which gives good weather and soil conditions for intensive production for export purposes. However, as is characteristic of tropical zones during the year, occur considerable variation in climate that may cause a significant impact on production.A detailed knowledge of the behavior of the weather in real-time could help to optimize the weather forecasting, production control (best times for planting and harvesting), the development of forecast systems for diseases and pests, recommendations for pesticides application, provide information for scientific research and in the prevention of emergencies and natural disasters.The BANACLIMA Program specializes in the storage and management of 12 automatic meteorological station installed at banana plantations distributed along the Caribbean basin. The network makes use of a Mikrotic WiFi system for data transmission and internet. BANACLIMA will be able to relate environmental parameters like rain, wind, temperature and relative humidity, solar radiation, and leaf wetness to aspects of crop management like planting, irrigation, fertilization, disease, and banana production.The main objective of the BANACLIMA Program make this information available to banana producers and crop managers; in real-time, allowing a continuous monitoring of weather conditions in the major banana producing areas in the country. The real-time information is put on the website of CORBANA ( www.corbana.co.cr ), facilitating the use of the information for the producer and farms managers farms to optimize the input, such as, fertilizers, nematicides and fungicides to obtain greater economic and environmental benefits. Another tool of the BANACLIMA is the e-mail and SMS with weather forecast, alerts and warning of extreme weather events and GIS maps.The information available in BANACLIMA contributes with the national banana industry to optimize the cultural plantation management, reducing costs and increasing production by the use of the precision agriculture.
During the last few years, the expansion of urban cover in the Quebrada Seca-Bermudez watershed has caused a series of floods that have damaged houses, bridges and other important infrastructure of the area. Hence local governments need a more precise description of these extreme rainfall events through reliable data and modeling. This study quantifies the discharge at several points in the Bermudez´s River watershed, based on 3 different storm durations and five different scenarios: three scenarios from previous years (2001, 2008 and 2012) and 2 forecasted scenarios for the year 2020 (one according to the projected urban growth and the other one based on local urban regulations). Land cover variations were determined using Lansat 7 ETM+ images. Both supervised and unsupervised classifications were applied to the satellite images and 6 common classes were obtained: forest, crops, pasture, urban, bare soil and industrial. The Curve Number was assigned based on this information and the soil data with a 1:20 000 scale resolution. A digital elevation model (DEM) with a 30 meters resolution was used to calculate the watershed parameters. Rainfall data over a period of almost 15 years from three meteorological stations were analyzed in order to obtain 2-, 5-, 10- and 25-year return periods. Discharge for all the scenarios was calculated with HEC-HMS program in order to evaluate the changes of urban growth. The results showed a rate of impervious cover of 27% for scenario 1 and 55% for scenario 2. The flow discharge increase for the year 2020 is expected to be between 1% to 14.9% for scenario 1.