Farm Environmental Impact Research Articles

Indicator-based agri-environmental direct payments: Assessment of three systems of different complexity levels

This paper presents a scientifically sound set of environmental indicators for comprehensive description of farm environmental impact within a policy-driven framework that aims at achieving Swiss agri-environmental policy goals. The indicator system covers the following key environmental areas: greenhouse gas (GHG) and ammonia emissions, nitrate and phosphorus leaching, biodiversity, plant protection products, soil erosion, and humus accumulation. Novel indicators were developed through reviewing existing indicators and intensive consultation with experts. To provide a flexible and suitable indicator-based system, three systems of varying complexity (simple, medium, detailed) were developed.In-depth evaluation revealed specific advantages and disadvantages of the three novel indicator systems at different complexity levels. The simple system benefited from a low administrative burden, but may suffer from limited acceptance owing to its low flexibility regarding farmers’ scope for action. The detailed system may be very demanding to implement in terms of data acquisition, but benefited from more accurate representation of the key driving processes. Successful implementation of the system will require broad acceptance promoted through sufficient support and advice, good communication between participating stakeholders, a secure and simple data acquisition process, and a high degree of transparency.

A Study on Water Use Efficiency and Pollution Control by Hydroponic Technology

At present day we are facing the pollution problems in major cities like Delhi, Hyderabad, Punjab, Haryana and some districts of U.P. These areas are having air pollution, water pollution and soil problems. The hydroponic technology like rain tower, vertical farming and aquaponics can help to control the pollution. As we have water resources of 400 million hectare meter and in 1190mm of annual rainfall 70 million hectare meter is lost to atmosphere through evaporation and transpiration, about 115 million hectare meter flows as surface run-off and remaining 215 million hectare meter soaks or infiltrates into the soil profile. Where in this technology the we reuse the water and reduces the loses of the water. This also helps in increases the yield and doubling the income of farmers in a sustainable ways. Precision agriculture means site specific management practice, its delivery the ideas of doing thing at right time and at right place. This helps the farmer avoid wasting resources and prevent run-off, ensuring that the soil has just the right amount of additives for optimum health, while also reducing costs and controlling the farm's environmental impact.

Preface: the WinMon.BE 2013 conference: steps towards an efficient and effective offshore wind farm environmental impact assessment

The race for a timely combat of climate change through the reduction of greenhouse gasses such as carbon dioxide has pushed renewable energy developments to a higher echelon. At present, wind is one of the most profitable sources of exploitable renewable energy. Wind farms are popping up at many places worldwide. Their expansion on land, however is constrained, because sites where wind farms do not conflict with the high-quality environmental standards of the local community are limited. Being uninhabited, the marine environment offers a vast area for wind farm development. Being uninhabited, however, does not equal not being subject to impact. The sea and oceans host rich ecosystems, many of which have and continue to deliver priceless and indispensable goods and services to mankind. Offshore wind farm development is hence subject to environmental impact assessment and residual impacts should be carefully monitored. Most, if not all, current offshore wind farms are accompanied by an environmental monitoring programme. These programmes should be considered the ‘finger on the pulse’ to avoid surpassing critical impact levels. They further ensure the achievement of the knowledge base needed to adjust new developments in a scientifically and environmentally sensitive and sound manner. On 26–28 November 2013, the Royal Belgian Institute of Natural Sciences organised the international scientific WinMon.BE 2013 conference on ‘‘Environmental impacts of offshorewind farms: learning from the past to optimise future monitoring programmes’’ (http:// odnature.naturalsciences.be/winmonbe2013).During the Guest editors: Steven Degraer, Jennifer Dannheim, Andrew B. Gill, Han Lindeboom & Dan Wilhelmsson / Environmental impacts of offshore wind farms

Wavelet based multi-spectral image analysis of maize leaf chlorophyll content

With recent advancement in precision farming, the need for variable rate technology has become apparent. Variable rate technology can improve the efficiency of farm operations and lessen farm's environmental impact. To implement effective variable rate applications, it is essential to gather and process information on crop nitrogen level reliably. This research is intended to develop an image-processing method to assess crop nitrogen level based on multi-spectral images of maize plants. This method first removed unnecessary information from the image and then converted the image into a one-dimensional (1D) signal representing the reflectance of the maize plant across leaves. The obtained data was further processed using the wavelet packet transform to find specific patterns that correspond to crop nitrogen stress. To implement wavelet analysis, the 1D signal was deconstructed into packets of narrow frequency bands to find the lowest level approximations at different levels. The maximum wavelet coefficients were identified for interested signal bands and then compared to SPAD meter readings, which were used as the ground-truth corn nitrogen level. Analysis results indicated that the db4 wavelet at a level 8 deconstruction had the highest linear regression coefficient ( R 2 = 0.78) with a high correlation coefficient ( r = 0.88) for corn nitrogen levels.