nitrate vulnerable zones

The positive effect of nitrogen fertilization in agriculture inevitably increases residual nitrogen losses. Water pollution led to legal restrictions of some farm practices within the framework of the Nitrates Directive of the EU. Nevertheless, even several decades later, the situation has not improved significantly. We present a possible science-based explanation of such a state and provide it to farmers and government as a support for environmental management settings. This study aimed to compare an established approach to implementing the Nitrates Directive, specifically the climate-based zoning of nitrogen fertilization restrictions using data from the mid-20th century. We evaluated this approach by juxtaposing the initial climate data with more recent data spanning from 1991 to 2020. Subsequently, we examined this zoning framework from the perspective of the non-vegetative period, characterized by temperatures below 5 °C, which is widely acknowledged as a critical threshold for nitrogen intake by plants. We found out that i) the employed climate-born zoning does not correspond to recent climate data; ii) nonvegetation period is longer than nitrogen fertilization restrictions. Therefore, despite a noteworthy 22 day reduction in the nonvegetation period from 1961/1962 to 2019/2020, we cast doubt on the notion that the period limiting nitrogen fertilizer application should also be shortened, while admitting that there are other abiotic and biotic factors affecting nitrogen behaviour within the ecosystem.

The positive effect of nitrogen fertilization in agriculture inevitably increases residual nitrogen losses. Water pollution led to legal restrictions of some farm practices within the framework of the Nitrates Directive of the EU. Nevertheless, even several decades later, the situation has not improved significantly. We present a possible science-based explanation of such a state and provide it to farmers and government as a support for environmental management settings. This study aimed to compare an established approach to implementing the Nitrates Directive, specifically the climate-based zoning of nitrogen fertilization restrictions using data from the mid-20th century. We evaluated this approach by juxtaposing the initial climate data with more recent data spanning from 1991 to 2020. Subsequently, we examined this zoning framework from the perspective of the non-vegetative period, characterized by temperatures below 5 °C, which is widely acknowledged as a critical threshold for nitrogen intake by plants. We found out that i) the employed climate-born zoning does not correspond to recent climate data; ii) nonvegetation period is longer than nitrogen fertilization restrictions. Therefore, despite a noteworthy 22 day reduction in the nonvegetation period from 1961/1962 to 2019/2020, we cast doubt on the notion that the period limiting nitrogen fertilizer application should also be shortened, while admitting that there are other abiotic and biotic factors affecting nitrogen behaviour within the ecosystem.

Nitrate leaching losses from arable lands into groundwater were a main driver in designating Nitrate Vulnerable Zones (NVZs) according to the Nitrates Directive, with a view to enhancing their water quality. Despite this, developing common strategies for effective water quality control in these areas remains a challenge in the European Union. This paper evaluates the performance of the Random Forest (RF) machine learning algorithm combined with Feature Selection (FS) techniques in predicting nitrate pollution in NVZs groundwater bodies in different periods and using updated environmental features in Andalusia, Spain. A set of forty-four features extrinsic to groundwater bodies were used as environmental predictors, with an aim to make this methodology exportable to other regions. Phenological features obtained through remote-sensing techniques were included to measure the dynamics of agricultural activity. In addition, other dynamic features derived from weather and livestock effluents were included to analyse seasonal and interannual changes in nitrate pollution. Three feature stacks and two nitrate databases were used in the predictive modelling: Period 1 (2009), with 321 nitrate samples for training; Period 2 (2010), with 282 nitrate samples for validation and initial spatial prediction; and Period 3 (2017), to assess the changes in the probability of groundwater nitrate content exceeding 50 mg/L. Random Forest as a wrapper with four sequential search methods was considered: sequential backward selection (SBS), sequential forward selection (SFS), sequential forward floating selection (SFFS) and sequential backward floating selection (SBFS). From among all the Feature Selection methods applied, Random Forest with SFS had the best performance (overall accuracy = 0.891 and six predictor features) and linked the highest probability of nitrate pollution with three dynamic features: the Normalized Difference Vegetation Index (NDVI) base level, NDVI value for the end of the growing season and accumulated manure production of livestock farms; and three static features: slope, sediment depositional areas and valley depth.

In intensive agriculture, N supply often exceeds crop requirements, even in nitrate vulnerable zones (NVZ). In farmland, the N surplus gives rise to NO 3´l eaching and consequent groundwater pollution. The present study aimed at proposing measures to reduce N leaching and hence improve N efficiency in a buffalo livestock farm located in the NVZ of Latina plain (Central Italy). The farm was cultivated with forage crops in a double annual crop rotation: Italian ryegrass (Lolium multiflorum Lam.) in winter and silage corn (Zea mays L.) in summer. Mineral and organic fertilizers were supplied to both crops. The annual N budget and soil solution NO 3-N concentrations were evaluated using a modeling approach. The performance of the WinEPIC model in simulating the response of the NO 3-N concentration in percolation to the N application rate was assessed and validated by field measurements of the NO 3-N concentration in the soil solution. Three scenarios were proposed to identify the best practice to minimize the environmental impact of N application without significant yield loss. Also, recommendations of best practices in N fertilization and animal manure spreading were given. This study thus provides useful preliminary information for decision-making in agriculture/environmental policies.

Excessive application of N fertilizer contributes substantially to high levels of nitrate (NO À 3-N) in surface and groundwater on Northern China. A trial was set up to quantify the fate of N within intensive wheat maize rotation system with a view to improve N and water use efficiencies. This paper describes the construction and testing of a lysimeter facility used for this trial. A 44 lysimeter/rain shelter facility was constructed at Shandong Agricultural University (SDAU). Each lysimeter was equipped with a neutron access tube for soil water monitoring and ceramic solution samplers for soil solutions collection. In order to precisely quantify water input, two rain shelters were used to exclude rainwater. The water balance showed that water outputs and inputs agree within 10% for all lysimeters, and that the average water used being 5% less than the total irrigation water supplied was considered as an acceptable error for such large lysimeters. Wheat grown in these devices was consistently higher than those grown with similar fertilizer management in a field located in Ling Xian due to enhanced soil fertility and irrigation in the devices. The facility was shown to be suitable for investigating water and nutrient balances of the root zone. 2014 Elsevier B.V. All rights reserved.

Nitrogen (N) fertilizers are one of the most expensive inputs in agricultural settings. Additionally, the loss of N increases costs, contributes to soil acidification, and causes off-site pollution of the air, groundwater and waterways. This study reviews current knowledge about technologies for N fertilization with potential to increase N use efficiency and reduce its negative effects on the environment. Classic inorganic sources such as urea and ammonium sulfate are the major sources utilized, while controlled N release fertilizers have not been significantly adopted for cereals and oil crops. Microorganisms, with the exception of Rhizobium sp. in soybeans, are also not widely used nowadays (e.g., plant growth-promoting bacteria and cynobacteria). The interest in implementing new N fertilization knowledge is stimulating the development of sensors to diagnose the N status and decision support systems for integrating several variables to optimize sources, rates and methods of application. Among potential new technologies we identified the incipient development of nanofertilizers, nutrient formulations to coat seeds, and recycled nutrients. Furthermore, increasing concern about the environmental consequences of N may facilitate the implementation of innovations outside the farm such as more effective regulations to guide N fertilization and methods to manufacture N fertilizers that are more energy-efficient and less CO 2 equivalent emitting.

Nitrogen is a vital nutrient helpful to plants and crop growth. However, among the leading causes of water resources pollution is the excess nitrogen from agricultural sources. In European Union countries, the Nitrates Directive has been approved to reduce this problem monitoring of water bodies with regard to nitrate concentrations, designation of Nitrate Vulnerable Zones (NVZs), and establishing codes of good agricultural practices and measures to prevent and reduce water pollution from nitrates. In light of this, we propose an integrated methodological approach to better manage a environmental issue as the perimeter of NVZs with the prospective that our approach could be used in the future by other member states representing a Best Practice in that direction. The methodology is based on data integration applied in a GIS environment. Different available data representing the knowledge of the territory were harmonised, systematised and georeferenced, in order to increase the enviro...

Farmers need to make decisions that in most cases incorporate the concept of prediction and can hardly be revoked. One such decision is the application of fertilizing inputs. During past crop management and decision-making on fertilizing practices, many significant errors have been recorded, which have led and continue to lead to reduced production and environmental burden. The methodology followed in this paper involves the use of GIS, fuzzy logic and expert knowledge, in order to model physical processes associated with nitrogen balance in cultivated ecosystems and to evaluate the capabilities of or limitations on the use of certain fertilizers, based on spatial criteria. An original spatial decision support system was designed, developed and applied in a given study area. The system is composed of two modules ("fertilizing rate" and "fertilizing type"), making use of soil, climate and cultivation practices' data, as recorded in the area of interest in quantitative or categorical form. The results of the application spatially classify the involved area according to its demand for nitrogen on the basis of the characteristics of each sub-region. The "fertilizing rate" module suggests reduced fertilizing doses of nitrogenous fertilizers compared to those already applied in the area. The system further divides the area into zones where specific types of fertilizers should be applied, giving a certain prescription for the method and time of application.

In intensive agriculture, N supply often exceeds crop requirements, even in nitrate vulnerable zones (NVZ). In farmland, the N surplus gives rise to NO 3´l eaching and consequent groundwater pollution. The present study aimed at proposing measures to reduce N leaching and hence improve N efficiency in a buffalo livestock farm located in the NVZ of Latina plain (Central Italy). The farm was cultivated with forage crops in a double annual crop rotation: Italian ryegrass (Lolium multiflorum Lam.) in winter and silage corn (Zea mays L.) in summer. Mineral and organic fertilizers were supplied to both crops. The annual N budget and soil solution NO 3-N concentrations were evaluated using a modeling approach. The performance of the WinEPIC model in simulating the response of the NO 3-N concentration in percolation to the N application rate was assessed and validated by field measurements of the NO 3-N concentration in the soil solution. Three scenarios were proposed to identify the best practice to minimize the environmental impact of N application without significant yield loss. Also, recommendations of best practices in N fertilization and animal manure spreading were given. This study thus provides useful preliminary information for decision-making in agriculture/environmental policies.

Nitrogen is an element present on Earth in different forms, such as gaseous in the air, dissolved in water, immobilized in the soil, as well as biologically bound in all living organisms. The transition from one form to another constitutes the nitrogen cycle. Current agricultural systems rely on nitrogen fertilizers, which represent the reactive or biologically available nitrogen in soil. The excessive presence of reactive nitrogen in the environment has become a threat to soil, water, and air. The increasing demands for food in the world are associated with significant increase in nitrogen fertilizers inputs which threatens the environment and living organisms. The quantities of nitrogen used per capita in developed countries exceed those in developing countries. However, developed countries are regulated by restrictions of fertilizers inputs in agriculture, whereas such regulations do not exist in most of the developing countries. The need to resort to alternative and eco-sustaina...

In intensive agriculture, N supply often exceeds crop requirements, even in nitrate vulnerable zones (NVZ). In farmland, the N surplus gives rise to NO 3´l eaching and consequent groundwater pollution. The present study aimed at proposing measures to reduce N leaching and hence improve N efficiency in a buffalo livestock farm located in the NVZ of Latina plain (Central Italy). The farm was cultivated with forage crops in a double annual crop rotation: Italian ryegrass (Lolium multiflorum Lam.) in winter and silage corn (Zea mays L.) in summer. Mineral and organic fertilizers were supplied to both crops. The annual N budget and soil solution NO 3-N concentrations were evaluated using a modeling approach. The performance of the WinEPIC model in simulating the response of the NO 3-N concentration in percolation to the N application rate was assessed and validated by field measurements of the NO 3-N concentration in the soil solution. Three scenarios were proposed to identify the best practice to minimize the environmental impact of N application without significant yield loss. Also, recommendations of best practices in N fertilization and animal manure spreading were given. This study thus provides useful preliminary information for decision-making in agriculture/environmental policies.

In intensive agriculture, N supply often exceeds crop requirements, even in nitrate vulnerable zones (NVZ). In farmland, the N surplus gives rise to NO 3 ´leaching and consequent groundwater pollution. The present study aimed at proposing measures to reduce N leaching and hence improve N efficiency in a buffalo livestock farm located in the NVZ of Latina plain (Central Italy). The farm was cultivated with forage crops in a double annual crop rotation: Italian ryegrass (Lolium multiflorum Lam.) in winter and silage corn (Zea mays L.) in summer. Mineral and organic fertilizers were supplied to both crops. The annual N budget and soil solution NO 3 -N concentrations were evaluated using a modeling approach. The performance of the WinEPIC model in simulating the response of the NO 3 -N concentration in percolation to the N application rate was assessed and validated by field measurements of the NO 3 -N concentration in the soil solution. Three scenarios were proposed to identify the best practice to minimize the environmental impact of N application without significant yield loss. Also, recommendations of best practices in N fertilization and animal manure spreading were given. This study thus provides useful preliminary information for decision-making in agriculture/environmental policies.

In intensive agriculture, N supply often exceeds crop requirements, even in nitrate vulnerable zones (NVZ). In farmland, the N surplus gives rise to NO 3´l eaching and consequent groundwater pollution. The present study aimed at proposing measures to reduce N leaching and hence improve N efficiency in a buffalo livestock farm located in the NVZ of Latina plain (Central Italy). The farm was cultivated with forage crops in a double annual crop rotation: Italian ryegrass (Lolium multiflorum Lam.) in winter and silage corn (Zea mays L.) in summer. Mineral and organic fertilizers were supplied to both crops. The annual N budget and soil solution NO 3-N concentrations were evaluated using a modeling approach. The performance of the WinEPIC model in simulating the response of the NO 3-N concentration in percolation to the N application rate was assessed and validated by field measurements of the NO 3-N concentration in the soil solution. Three scenarios were proposed to identify the best practice to minimize the environmental impact of N application without significant yield loss. Also, recommendations of best practices in N fertilization and animal manure spreading were given. This study thus provides useful preliminary information for decision-making in agriculture/environmental policies.