Abstract

Syngenta is committed to increasing crop productivity and to using limited resources such as land, water and inputs more efficiently. Since 2014, Syngenta has been measuring trends in agricultural input efficiency on a global network of real farms.

The Good Growth Plan dataset shows aggregated productivity and resource efficiency indicators by harvest year. The data has been collected from more than 700 farms and covers more than 10 different crops in 7 African countries.

Geographic coverage

National coverage

Analysis unit

Agricultural holdings

Kind of data

Sample survey data [ssd]

Sampling procedure

A. Sample design Farms are grouped in clusters, which represent a crop grown in an area with homogenous agro- ecological conditions and include comparable types of farms. The sample includes reference and benchmark farms.

B. Sample size Sample sizes for each cluster are determined with the aim to measure statistically significant increases in crop efficiency over time. This is done based on target productivity increases and assumptions regarding the variability of farm metrics in each cluster. The smaller the expected increase, the larger the sample size needed to measure significant differences over time. Variability within clusters is assumed based on public research and expert opinion. In addition, growers are also grouped in clusters as a means of keeping variances under control, as well as distinguishing between growers in terms of crop size, region and technological level. A minimum sample size of 20 interviews per cluster is needed. The minimum number of reference farms is 5 of 20. The optimal number of reference farms is 10 of 20 (balanced sample).

Mode of data collection

Computer Assisted Personal Interview [capi]

Abstract

Syngenta is committed to increasing crop productivity and to using limited resources such as land, water and inputs more efficiently. Since 2014, Syngenta has been measuring trends in agricultural input efficiency on a global network of real farms.

The Good Growth Plan dataset shows aggregated productivity and resource efficiency indicators by harvest year. The data has been collected from more than 700 farms and covers more than 10 different crops in 7 African countries.

Geographic coverage

National Coverage

Analysis unit

Agricultural holdings

Kind of data

Sample survey data [ssd]

Sampling procedure

A. Sample design Farms are grouped in clusters, which represent a crop grown in an area with homogenous agro- ecological conditions and include comparable types of farms. The sample includes reference and benchmark farms.

B. Sample size Sample sizes for each cluster are determined with the aim to measure statistically significant increases in crop efficiency over time. This is done based on target productivity increases and assumptions regarding the variability of farm metrics in each cluster. The smaller the expected increase, the larger the sample size needed to measure significant differences over time. Variability within clusters is assumed based on public research and expert opinion. In addition, growers are also grouped in clusters as a means of keeping variances under control, as well as distinguishing between growers in terms of crop size, region and technological level. A minimum sample size of 20 interviews per cluster is needed. The minimum number of reference farms is 5 of 20. The optimal number of reference farms is 10 of 20 (balanced sample).

Mode of data collection

Computer Assisted Personal Interview [capi]

Abstract

Syngenta is committed to increasing crop productivity and to using limited resources such as land, water and inputs more efficiently. Since 2014, Syngenta has been measuring trends in agricultural input efficiency on a global network of real farms.

The Good Growth Plan dataset shows aggregated productivity and resource efficiency indicators by harvest year. The data has been collected from more than 700 farms and covers more than 10 different crops in 7 African countries.

Geographic coverage

National Coverage

Analysis unit

Agricultural holdings

Kind of data

Sample survey data [ssd]

Sampling procedure

A. Sample design Farms are grouped in clusters, which represent a crop grown in an area with homogenous agro- ecological conditions and include comparable types of farms. The sample includes reference and benchmark farms.

B. Sample size Sample sizes for each cluster are determined with the aim to measure statistically significant increases in crop efficiency over time. This is done based on target productivity increases and assumptions regarding the variability of farm metrics in each cluster. The smaller the expected increase, the larger the sample size needed to measure significant differences over time. Variability within clusters is assumed based on public research and expert opinion. In addition, growers are also grouped in clusters as a means of keeping variances under control, as well as distinguishing between growers in terms of crop size, region and technological level. A minimum sample size of 20 interviews per cluster is needed. The minimum number of reference farms is 5 of 20. The optimal number of reference farms is 10 of 20 (balanced sample).

Mode of data collection

Computer Assisted Personal Interview [capi]

Abstract

Syngenta is committed to increasing crop productivity and to using limited resources such as land, water and inputs more efficiently. Since 2014, Syngenta has been measuring trends in agricultural input efficiency on a global network of real farms.

The Good Growth Plan dataset shows aggregated productivity and resource efficiency indicators by harvest year. The data has been collected from more than 700 farms and covers more than 10 different crops in 7 African countries.

Geographic coverage

National coverage

Analysis unit

Agricultural holdings

Kind of data

Sample survey data [ssd]

Sampling procedure

A. Sample design Farms are grouped in clusters, which represent a crop grown in an area with homogenous agro- ecological conditions and include comparable types of farms. The sample includes reference and benchmark farms.

B. Sample size Sample sizes for each cluster are determined with the aim to measure statistically significant increases in crop efficiency over time. This is done based on target productivity increases and assumptions regarding the variability of farm metrics in each cluster. The smaller the expected increase, the larger the sample size needed to measure significant differences over time. Variability within clusters is assumed based on public research and expert opinion. In addition, growers are also grouped in clusters as a means of keeping variances under control, as well as distinguishing between growers in terms of crop size, region and technological level. A minimum sample size of 20 interviews per cluster is needed. The minimum number of reference farms is 5 of 20. The optimal number of reference farms is 10 of 20 (balanced sample).

Mode of data collection

Computer Assisted Personal Interview [capi]

The Medium Term Development Plan III (MTDP III) captures the main thrust of the Alotau Accord II and sets the Goal of “Securing our future through inclusive sustainable economic growth” by focusing on key investments to further stimulate the economic growth in the medium term. The key priorities of the Alotau Accord II are (1) inclusive Economic Growth with renewed focus in Agriculture, (2) continuing with Infrastructure development, (3) improvement of quality of Health Care, (4) improvement of quality of Education and Skills Development, and (5) improvement of Law and Order.

Building on the gains and experiences of MTDP I and II (2012- 2017) and the priorities of Alotau Accord II, this Plan was formulated taking into account also the principles of inclusiveness and sustainability prescribed by the Strategy for Responsible Sustainable Development (StaRS) and the United Nations Sustainable Development Goals (SDG). Under the MTDP III, the Government will focus on: (1) Increasing the revenue base and improving revenue collection, (2) increasing exports, (3) reducing imports, (4) improving and increasing opportunities for citizens to create wealth, and (5) improving the quality and effectiveness in the delivery of public goods and services.

Abstract

Syngenta is committed to increasing crop productivity and to using limited resources such as land, water and inputs more efficiently. Since 2014, Syngenta has been measuring trends in agricultural input efficiency on a global network of real farms.

The Good Growth Plan dataset shows aggregated productivity and resource efficiency indicators by harvest year. The data has been collected from more than 700 farms and covers more than 10 different crops in 7 African countries.

Geographic coverage

National coverage

Analysis unit

Agricultural holdings

Kind of data

Sample survey data [ssd]

Sampling procedure

A. Sample design Farms are grouped in clusters, which represent a crop grown in an area with homogenous agro- ecological conditions and include comparable types of farms. The sample includes reference and benchmark farms.

B. Sample size Sample sizes for each cluster are determined with the aim to measure statistically significant increases in crop efficiency over time. This is done based on target productivity increases and assumptions regarding the variability of farm metrics in each cluster. The smaller the expected increase, the larger the sample size needed to measure significant differences over time. Variability within clusters is assumed based on public research and expert opinion. In addition, growers are also grouped in clusters as a means of keeping variances under control, as well as distinguishing between growers in terms of crop size, region and technological level. A minimum sample size of 20 interviews per cluster is needed. The minimum number of reference farms is 5 of 20. The optimal number of reference farms is 10 of 20 (balanced sample).

Mode of data collection

Computer Assisted Personal Interview [capi]

Development Plan 2022-2028 - Core Bus Corridors DLR. Published by Dún Laoghaire-Rathdown County Council. Available under the license cc-by (CC-BY-4.0).Core Bus Corridors as per the adopted Dún Laoghaire–Rathdown County Council Development Plan 2022-2028 (https://www.dlrcoco.ie/CDP2022-2028).

These geospatial layers are indicative of the published version of the maps. If there are any discrepancies between this layer and the published maps, the published maps shall prevail. The Core Bus Corridors may be subject to change. It should be noted that the core bus corridors incorporate the existing quality bus corridors on the N11 and Rock Road. Dún Laoghaire – Rathdown County Council (DLR CoCo) provides this data for information only. DLR CoCo makes no guaranteed as to the accuracy, timeliness or completeness of any of the data. DLR CoCo shall have no liability for the data or lack thereof, or any decision made or action taken or not taken in reliance upon any of the data. This data may not be used for any other purpose without prior permission....

Views & Prospects as per the adopted Dún Laoghaire–Rathdown County Council Development Plan 2022-2028 (https://www.dlrcoco.ie/CDP2022-2028).

Dún Laoghaire – Rathdown County Council (DLR CoCo) contains many sites and vantage points from which scenic views over areas of great natural beauty, local landmarks, historic landscapes, adjoining Counties, and the City of Dublin may be obtained. In addition, the County also contains important prospects i.e. prominent landscapes or areas of special amenity value, or special interest which are widely visible from the surrounding area. Specific Views and Prospects for protection have been identified in the Plan and are considered when assessing planning applications. These geospatial layers are indicative of the published version of the maps. If there are any discrepancies between this layer and the published maps, the published maps shall prevail.

DLR CoCo provides this data for information only. DLR CoCo makes no guaranteed as to the accuracy, timeliness or completeness of any of the data. DLR CoCo shall have no liability for the data or lack thereof, or any decision made or action taken or not taken in reliance upon any of the data. This data may not be used for any other purpose without prior permission.

Abstract

Syngenta is committed to increasing crop productivity and to using limited resources such as land, water and inputs more efficiently. Since 2014, Syngenta has been measuring trends in agricultural input efficiency on a global network of real farms.

The Good Growth Plan dataset shows aggregated productivity and resource efficiency indicators by harvest year. The data has been collected from more than 700 farms and covers more than 10 different crops in 7 African countries.

Geographic coverage

National coverage

Analysis unit

Agricultural holdings

Kind of data

Sample survey data [ssd]

Sampling procedure

A. Sample design Farms are grouped in clusters, which represent a crop grown in an area with homogenous agro- ecological conditions and include comparable types of farms. The sample includes reference and benchmark farms.

B. Sample size Sample sizes for each cluster are determined with the aim to measure statistically significant increases in crop efficiency over time. This is done based on target productivity increases and assumptions regarding the variability of farm metrics in each cluster. The smaller the expected increase, the larger the sample size needed to measure significant differences over time. Variability within clusters is assumed based on public research and expert opinion. In addition, growers are also grouped in clusters as a means of keeping variances under control, as well as distinguishing between growers in terms of crop size, region and technological level. A minimum sample size of 20 interviews per cluster is needed. The minimum number of reference farms is 5 of 20. The optimal number of reference farms is 10 of 20 (balanced sample).

Mode of data collection

Computer Assisted Personal Interview [capi]

Layer showing locations of existing Trees and Woodlands as per the adopted Dún Laoghaire–Rathdown County Council Development Plan 2022-2028 (https://www.dlrcoco.ie/CDP2022-2028). Trees, groups of trees or woodlands which form a significant feature in the landscape or are important in setting the character or ecology of an area. The tree symbols may represent one tree or a group of trees which make a contribution to the area. These geospatial layers are indicative of the published version of the maps. If there are any discrepancies between this layer and the published maps, the published maps shall prevail. Dún Laoghaire – Rathdown County Council (DLR CoCo) provides this data for information only. DLR CoCo makes no guaranteed as to the accuracy, timeliness or completeness of any of the data. DLR CoCo shall have no liability for the data or lack thereof, or any decision made or action taken or not taken in reliance upon any of the data. This data may not be used for any other purpose without prior permission.

Proposed and Existing LAP boundaries, Sandyford Urban Framework Plan Boundary & Cherrywood SDZ Boundary from Dun Laoghaire-Rathdown County Council County Development Plan 2022 - 2028 (https://www.dlrcoco.ie/CDP2022-2028). A Local Area Plan (LAP) consists of a suite of policies and objectives for an area, intended to guide that area’s development for a period of 6 years, which may be extended to a maximum of 10 years if appropriate. Like the ‘County Development Plan’ (CDP), which spans the entirety of Dún LaoghaireRathdown, a LAP is a statutory planning document, but for a smaller, more focused area, and with greater detail. The boundaries of any proposed Local Area Plans are indicative only and may be subject to change. These geospatial layers are indicative of the published version of the maps. If there are any discrepancies between this layer and the published maps, the published maps shall prevail. Dún Laoghaire – Rathdown County Council (DLR CoCo) provides this data for information only. DLR CoCo makes no guaranteed as to the accuracy, timeliness or completeness of any of the data. DLR CoCo shall have no liability for the data or lack thereof, or any decision made or action taken or not taken in reliance upon any of the data. This data may not be used for any other purpose without prior permission

Layer showing the locations of Specific Local Objective symbols (SLO) as per the adopted Dún Laoghaire–Rathdown County Council Development Plan 2022-2028 (https://www.dlrcoco.ie/CDP2022-2028). These geospatial layers are indicative of the published version of the maps. If there are any discrepancies between this layer and the published maps, the published maps shall prevail. Dún Laoghaire – Rathdown County Council (DLR CoCo) provides this data for information only. DLR CoCo makes no guaranteed as to the accuracy, timeliness or completeness of any of the data. DLR CoCo shall have no liability for the data or lack thereof, or any decision made or action taken or not taken in reliance upon any of the data. This data may not be used for any other purpose without prior permission.

Development Plan 2022-2028 - Right of Way DLR. Published by Dún Laoghaire-Rathdown County Council. Available under the license cc-by (CC-BY-4.0).Data showing location of all Rights of Way (ROW) as per the adopted Dún Laoghaire–Rathdown County Council Development Plan 2022-2028 (https://www.dlrcoco.ie/CDP2022-2028).

This data includes all Public Rights of Way, Recreation Access Routes & Wicklow Way Routes.

These geospatial layers are indicative of the published version of the maps. If there are any discrepancies between this layer and the published maps, the published maps shall prevail. Dún Laoghaire – Rathdown County Council (DLR CoCo) provides this data for information only. DLR CoCo makes no guaranteed as to the accuracy, timeliness or completeness of any of the data. DLR CoCo shall have no liability for the data or lack thereof, or any decision made or action taken or not taken in reliance upon any of the data. This data may not be used for any other purpose without prior permission....

Development Plan 2022-2028 - Institutional Lands DLR. Published by Dún Laoghaire-Rathdown County Council. Available under the license cc-by (CC-BY-4.0).Institutional lands as per the adopted Dún Laoghaire–Rathdown County Council Development Plan 2022-2028 (https://www.dlrcoco.ie/CDP2022-2028).

These geospatial layers are indicative of the published version of the maps. If there are any discrepancies between this layer and the published maps, the published maps shall prevail. Dún Laoghaire – Rathdown County Council (DLR CoCo) provides this data for information only. DLR CoCo makes no guaranteed as to the accuracy, timeliness or completeness of any of the data. DLR CoCo shall have no liability for the data or lack thereof, or any decision made or action taken or not taken in reliance upon any of the data. This data may not be used for any other purpose without prior permission....

Areas Suitable for Windfarm Development as defined as part of the Renewable Energy Strategy included with the Roscommon County Development Plan 2022-2028. Published by Roscommon County Council | Dataset in English | Spatial projection is Irish Transverse Mercator | Created 2022 | Updated as required. Roscommon County Council provides this information with the understanding that it is not guaranteed to be accurate, correct or complete. Roscommon County Council accepts no liability for any loss or damage suffered by those using this data for any purpose.

Development Plan 2022-2028 - The Metals DLR. Published by Dún Laoghaire-Rathdown County Council. Available under the license cc-by (CC-BY-4.0).The Metals Candidate Architectural Conservation Area (cACA) as per the adopted Dún Laoghaire–Rathdown County Council Development Plan 2022-2028 (https://www.dlrcoco.ie/CDP2022-2028).

These geospatial layers are indicative of the published version of the maps. If there are any discrepancies between this layer and the published maps, the published maps shall prevail. Dún Laoghaire – Rathdown County Council (DLR CoCo) provides this data for information only. DLR CoCo makes no guaranteed as to the accuracy, timeliness or completeness of any of the data. DLR CoCo shall have no liability for the data or lack thereof, or any decision made or action taken or not taken in reliance upon any of the data. This data may not be used for any other purpose without prior permission....

Development Plan 2022-2028 - Mews Development DLR. Published by Dún Laoghaire-Rathdown County Council. Available under the license cc-by (CC-BY-4.0).Mews Developments as per the adopted Dún Laoghaire–Rathdown County Council Development Plan 2022-2028 (https://www.dlrcoco.ie/CDP2022-2028).

These geospatial layers are indicative of the published version of the maps. If there are any discrepancies between this layer and the published maps, the published maps shall prevail. Dún Laoghaire – Rathdown County Council (DLR CoCo) provides this data for information only. DLR CoCo makes no guaranteed as to the accuracy, timeliness or completeness of any of the data. DLR CoCo shall have no liability for the data or lack thereof, or any decision made or action taken or not taken in reliance upon any of the data. This data may not be used for any other purpose without prior permission....

Development Plan 2022-2028 - Proposed Education Sites DLR. Published by Dún Laoghaire-Rathdown County Council. Available under the license cc-by (CC-BY-4.0).Proposed Education Sites as per the adopted Dún Laoghaire–Rathdown County Council Development Plan 2022-2028 (https://www.dlrcoco.ie/CDP2022-2028).

These geospatial layers are indicative of the published version of the maps. If there are any discrepancies between this layer and the published maps, the published maps shall prevail. Dún Laoghaire – Rathdown County Council (DLR CoCo) provides this data for information only. DLR CoCo makes no guaranteed as to the accuracy, timeliness or completeness of any of the data. DLR CoCo shall have no liability for the data or lack thereof, or any decision made or action taken or not taken in reliance upon any of the data. This data may not be used for any other purpose without prior permission....