Organic farming phd thesis

However, adoption of certified organic farming is not an easy option for farmers and it carries with it several barriers such as technical, economic, social, cultural or legal. The Jordanian Government is interested in proposing organic farming to farmers, but without initially investigating whether or not organic farming will be a suitable system for farmers. To do so, a two-stage research methodology was employed in this research to gain the necessary data during two periods of fieldwork, April to September and July to September During this fieldwork, interviews with 46 farmers using an open questionnaire and interviews with discussion groups and government officials were conducted to investigate barriers and potential for organic farming in Jordan.

Also, a national workshop was conducted attended by the Minister of Agriculture and stakeholders to evaluate and to ensure the sustainability of the proposed model. Findings also showed that despite barriers the area has potential for organic production owing to its extensive area, good water quality, potential farmers and international agreements. Based on the results obtained from this research, a suitable organic farming model for Jordan, and other countries having similar conditions, was developed. For each effect size, we extracted taxonomic and functional data on the study organism s.

We also recorded i the sampling unit of the species richness data e. Data on species richness were extracted from the text, tables or figures in publications using the program getdata graph digitizer 2. Other measures of variation presented in publications were converted to standard deviations. The information on taxonomic groups was used to create categorical covariates for different higher taxonomic units and ecological functions.

For taxonomic groups, we classified species as: arthropods, birds, microbes and plants. The functional classification is based on the idea that different organism groups may contribute to different ecosystem services. We also separated the data according to crop type. Many studies include multiple records for different organism groups or crop types on the same farm.

As a result, our data set of 94 studies was subdivided into observations see Statistical Analysis for more details. However, an intensively farmed region is likely to include fewer habitats than a more extensively farmed area. The average field size may reflect the overall extent of farming on the landscape but, depending on local farming practices, not necessarily farming intensity.

To calculate the three metrics, we first identified a standardized sampling space at each location based on descriptions in the original publications.

Where coordinates were not provided, we identified an area that we were confident, included the study area based on descriptions in the text. We then identified a central measuring point, making sure it was placed in a landscape with agricultural fields, and the radius in metres defining the appropriate area for sampling around this point.

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If no information about the area of the study region was available, we visually examined the Google Map image and set the radius so that the included landscape was representative of its complexity and similar to the landscape closest to the central point. The positions of the five transects were defined by sets of three randomly generated numbers. The first number, randomly selected between 0 central measuring point and the radius of the study region, denoted how many metres from the central point the starting point of each transect would be situated.

The second number specified the angle degrees , defining the direction relative to the central point for which the start point of the transect should be placed. Combined, these two random numbers created a bearing, from the centre of the study region, that defined the transect location. Transects were not allowed to cross. The transects sampled were line transects with no surrounding buffer. In some cases, several studies had been conducted in the same area, in which case the same landscape data were used. When publications that garnered multiple observations had been conducted in multiple regions, and data specified per region, we collected landscape data per region.

If the study region was not specified at all — but only the country — we used the mean values of all other studies in that country. The Google Maps analysed were always the most recent images available. This represents one caveat in our landscape analysis: for older studies, there is a time lag between the date the study was conducted and the date our landscape data were collected. Many of the early studies were conducted in Europe, a region that we would expect to show the least landscape change in agricultural areas over the relevant time span. On the log scale, an effect size of 0 means no difference and a positive value means that the organic farm has higher species richness than the conventional farm.

The log response ratio displays bias at small sample sizes, when the normal approximation to the distribution of the effect size deviates from the exact distribution. Our analysis was carried out using r 3.


The models were fitted to the data using the function metahdep. We analysed separate observations for subgroups within studies — that is, different taxonomic groups or crop types. A random effect was used to account for differences across studies, for example, among farming systems included within organic and conventional groups. Variables of interest, selected a priori , were included in a metaregression to see whether they explained any differences in biodiversity on organic vs.

There is hierarchical dependence between multiple observations within studies. Having several effect sizes obtained from the same publication violates the assumption that effect sizes are independent. By incorporating this hierarchical variance structure, we could disentangle important differences between organisms and crop types without assuming independence of observations.

A simple ecological example would be a lack of studies representing a system relative to its global importance; this is a bias produced by consensus in the literature that is not founded on a representative sample of reality. It should also describe bias in the literature and indicate where that bias may lie. We investigated bias in both the forms described above. We also estimated the slope of the relationship between sampling variance and effect size.

Combining these diagnostics allowed us to explore asymmetry in the data and then, under the assumption that this is due to publication bias, assess its impact on our result. We used this comparison to discuss how representative the current literature is of global organic farming trends.

These results reveal substantial heterogeneity among effect sizes, although many studies showed a large positive effect of organic farming on biodiversity relative to conventional farming. We found large differences in the effect of organic farming on different taxonomic and functional groups Fig. For example, among taxonomic groups, plants benefited the most from organic farming Fig. Arthropods, birds and microbes also showed a substantial positive effect. Disaggregating organisms into functional groups showed a variety of responses: among functional groups, the largest effect size was found for pollinators while decomposers showed little effect Fig.

The crop types showed varying responses, with large positive effect sizes in cereals and mixed farming, and moderate positive effect sizes for all others Fig.

The grand mean is shown in black, accompanied by the black line. The dashed lines show the zero line. When the percentage of arable fields was fitted as an interaction with functional group, there was substantial heterogeneity in the resulting slopes.

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The geographical representation in our data set, however, showed much less congruence Fig. The funnel plot Fig. This suggests that, if publication bias is evident, the reported effect size is robust to its impact. This was the minimum value obtained from the cumulative plot and was reached after c. This reduced effect size did not greatly alter our interpretation of the magnitude of organic farming's positive effect on biodiversity. There is therefore no sign of a dwindling effect size with the addition of further evidence.


This implies that the increase in diversity with organic farming that we report here is robust, given the choice of crops and study areas included see below for a discussion of the representativeness of our study. Only the proportion of arable fields in the landscape had any significant overall effect.

The difference in diversity between organic and conventional farming generally increased with increasing proportion of arable fields, although there was large variation around the estimated slope. These differences may be due to the importance of local actions relative to regional actions and to the movement of organisms and chemicals across the landscape. For example, some pollinators are known to be sensitive to certain pesticides Goulson , leading to an EU moratorium on neonicotinoids.

Organic principles specify that farming systems should be self-sufficient, as far as possible, with regard to animal feed, fertilisers and manures. Moreover, conventional stockless arable farms depend on the input of synthetic nitrogen fertilizers, while stockpiled manure and slurry on livestock farms can create additional emissions and other environmental problems- organic farms mitigate such problems by on-farm or cooperative use of farmyard manure between crop and livestock operations.

However, specialised organic farms also exist and particular care is required on these holdings to reduce potential negative climate change impacts.

Varthur Narayana Reddy On Organic Farming

Less fossil energy is used within organic systems than non-organic systems, on a per hectare and, in most cases, on a per unit of food produced basis. The lower energy use on organic farms is largely because energy inputs for industrial manufacture of fertilizers and pesticides are avoided. Organic agriculture has a significant contribution to make in this respect: practices that are commonly used on organic farms use of organic fertilizers, fertility building leys with legumes and cover crops further the production of soil organic matter which removes CO2 from the atmosphere and stores the carbon as biomass and soil organic carbon in the soil, reducing its release back into the atmosphere.

The production of livestock or food crops on land that also grows trees for timber, firewood, or other tree products agro-forestry may also increase soil carbon sequestration; the standing stock of carbon above ground is usually higher than the equivalent land use without trees. The Organic Research Centre is currently exploring the benefits that agro-forestry can provide in this area. In April the English agriculture industry published its voluntary action plan for reducing greenhouse gases. It contains a commitment to reducing agricultural greenhouse gas emissions by three million tonnes of CO2 equivalent per year from The Action Plan aims to meet this target without compromising domestic production, as it is too simple a solution to produce less and import more.

Dynamics of Danish Agricultural Landscapes and the Role of Organic

Instead the Action Plan focuses on how farmers, across all sectors and farming systems, can become more efficient to help reduce greenhouse gas emissions and make cost savings per unit of production. The Round Table aims to promote the potential of organic farming to mitigate and adapt to climate change, and build awareness of the advantage of organic farming systems in this context. The Round Table also advises the international community on organic agriculture and climate change issues, to help initiate changes in policy and wider support of organic agriculture and advise the development of climate-related provisions in international standards.

The Round Table also aims to support management practices and standard development issues that look at improving organic standards from a climate change perspective. The Organic Research Centre has been involved with the development of the Round Table since and are helping to build an evidence base for the benefits of organic agriculture with regard to the climate change.