Good Growth Plan, 2022 - Algeria

Abstract
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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
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National Coverage

Analysis unit
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Agricultural holdings

Kind of data
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Sample survey data [ssd]

Sampling procedure
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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
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Computer Assisted Personal Interview [capi]

{'Abstract': '先正达致力于提升作物产量，并更高效地利用诸如土地、水资源等有限资源。自2014年起，先正达在全球范围内真实农场的网络中监测农业投入效率的趋势。', 'Geographic_coverage': '国家覆盖范围', 'Analysis_unit': '农业经营单位', 'Kind_of_data': '样本调查数据[ssd]', 'Sampling_procedure': {'A. Sample_design': '农场被分组成簇，这些簇代表了在具有同质农业生态条件的区域内种植的作物，并包括可比类型的农场。样本包括参考和基准农场。', 'B. Sample_size': '每个簇的样本大小是根据测量作物效率随时间统计显著增加的目标来确定的。这是基于目标产量增加和每个簇中农场指标的变异性假设进行的。预期的增加越小，所需的样本量就越大，以便在时间上测量显著差异。簇内的变异性基于公开研究和专家意见进行假设。此外，种植者也被分组成簇，作为控制方差和区分种植者在作物规模、地区和技术水平方面的手段。每个簇至少需要20次访谈的样本量。参考农场的最少数量为20个中的5个。参考农场的最佳数量为20个中的10个（平衡样本）。', 'Mode_of_data_collection': '计算机辅助个人访谈[capi]'}}