Good Growth Plan 2014-2019 - Egypt, Arab Rep.

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 4,000 farms and covers more than 20 different crops in 46 countries. The data (except USA data and for Barley in UK, Germany, Poland, Czech Republic, France and Spain) was collected, consolidated and reported by Kynetec (previously Market Probe), an independent market research agency. It can be used as benchmarks for crop yield and input efficiency.

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. The reference farms were selected by Syngenta and the benchmark farms were randomly selected by Kynetec within the same cluster.

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 by Kynetec 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).

C. Selection procedure
The respondents were picked randomly using a “quota based random sampling” procedure. Growers were first randomly selected and then checked if they complied with the quotas for crops, region, farm size etc. To avoid clustering high number of interviews at one sampling point, interviewers were instructed to do a maximum of 5 interviews in one village.

BF Screened from Egypt were from Assuit, Menia, Banisuif, Sharqiya, Ismaliaand were selected based on the following criterion:
- BACKGROUND: Open field tomatoes
- Flood irrigation
- Ploughing with a tractor or manually (e.g. with a hoe)
- Usage of chemical and/or organic fertilizers
- Selling the harvest is the main after harvest activity

Mode of data collection
---------------------------

Face-to-face [f2f]

Research instrument
---------------------------

Data collection tool for 2019 covered the following information:

(A) PRE- HARVEST INFORMATION

PART I: Screening
PART II: Contact Information
PART III: Farm Characteristics
a. Biodiversity conservation
b. Soil conservation
c. Soil erosion
d. Description of growing area
e. Training on crop cultivation and safety measures
PART IV: Farming Practices - Before Harvest
a. Planting and fruit development - Field crops
b. Planting and fruit development - Tree crops
c. Planting and fruit development - Sugarcane
d. Planting and fruit development - Cauliflower
e. Seed treatment

(B) HARVEST INFORMATION

PART V: Farming Practices - After Harvest
a. Fertilizer usage
b. Crop protection products
c. Harvest timing & quality per crop - Field crops
d. Harvest timing & quality per crop - Tree crops
e. Harvest timing & quality per crop - Sugarcane
f. Harvest timing & quality per crop - Banana
g. After harvest
PART VI - Other inputs - After Harvest
a. Input costs
b. Abiotic stress
c. Irrigation

See all questionnaires in external materials tab

Cleaning operations
---------------------------

Data processing:

Kynetec uses SPSS (Statistical Package for the Social Sciences) for data entry, cleaning, analysis, and reporting. After collection, the farm data is entered into a local database, reviewed, and quality-checked by the local Kynetec agency. In the case of missing values or inconsistencies, farmers are re-contacted. In some cases, grower data is verified with local experts (e.g. retailers) to ensure data accuracy and validity. After country-level cleaning, the farm-level data is submitted to the global Kynetec headquarters for processing. In the case of missing values or inconsistences, the local Kynetec office was re-contacted to clarify and solve issues.

Quality assurance
Various consistency checks and internal controls are implemented throughout the entire data collection and reporting process in order to ensure unbiased, high quality data.

• Screening: Each grower is screened and selected by Kynetec based on cluster-specific criteria to ensure a comparable group of growers within each cluster. This helps keeping variability low.

• Evaluation of the questionnaire: The questionnaire aligns with the global objective of the project and is adapted to the local context (e.g. interviewers and growers should understand what is asked). Each year the questionnaire is evaluated based on several criteria, and updated where needed.

• Briefing of interviewers: Each year, local interviewers - familiar with the local context of farming -are thoroughly briefed to fully comprehend the questionnaire to obtain unbiased, accurate answers from respondents.

• Cross-validation of the answers:
o Kynetec captures all growers' responses through a digital data-entry tool. Various logical and consistency checks are automated in this tool (e.g. total crop size in hectares cannot be larger than farm size)
o Kynetec cross validates the answers of the growers in three different ways:
1. Within the grower (check if growers respond consistently during the interview)
2. Across years (check if growers respond consistently throughout the years)
3. Within cluster (compare a grower's responses with those of others in the group)
o All the above mentioned inconsistencies are followed up by contacting the growers and asking them to verify their answers. The data is updated after verification. All updates are tracked.

• Check and discuss evolutions and patterns: Global evolutions are calculated, discussed and reviewed on a monthly basis jointly by Kynetec and Syngenta.

• Sensitivity analysis: sensitivity analysis is conducted to evaluate the global results in terms of outliers, retention rates and overall statistical robustness. The results of the sensitivity analysis are discussed jointly by Kynetec and Syngenta.

• It is recommended that users interested in using the administrative level 1 variable in the location dataset use this variable with care and crosscheck it with the postal code variable.

Data appraisal
---------------------------

Due to the above mentioned checks, irregularities in fertilizer usage data were discovered which had to be corrected:

For data collection wave 2014, respondents were asked to give a total estimate of the fertilizer NPK-rates that were applied in the fields. From 2015 onwards, the questionnaire was redesigned to be more precise and obtain data by individual fertilizer products. The new method of measuring fertilizer inputs leads to more accurate results, but also makes a year-on-year comparison difficult. After evaluating several solutions to this problems, 2014 fertilizer usage (NPK input) was re-estimated by calculating a weighted average of fertilizer usage in the following years.

摘要
---------------------------
先正达致力于提升作物产量，并更高效地利用如土地、水资源等有限资源。自2014年起，先正达在全球真实农场网络中测量农业投入效率的趋势。良好增长计划数据集展示了按收获年度汇总的生产力和资源效率指标。数据来自超过4,000个农场，覆盖了46个国家的20多种作物。数据（除美国数据及英国、德国、波兰、捷克共和国、法国和西班牙的燕麦数据外）由独立市场研究机构Kynetec（原名市场调研）收集、整合并报告。这些数据可以作为作物产量和投入效率的基准。
地理覆盖范围
---------------------------
全国覆盖
分析单元
---------------------------
农业经营
数据类型
---------------------------
样本调查数据 [ssd]
抽样程序
---------------------------
A. 样本设计
农场被分为代表在具有同质农业生态条件的区域内种植的作物的集群，包括可比类型的农场。样本包括参照和基准农场。参照农场由先正达选择，而基准农场由Kynetec在同一集群内随机选择。
B. 样本量
每个集群的样本量是根据测量随时间推移作物效率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集群中农场指标变异性假设由Kynetec进行的。预期增加越小，需要测量的显著差异的样本量就越大。集群内的变异性基于公共研究和专家意见。此外，种植者也按集群分组，作为控制方差的一种手段，以及根据作物规模、地区和技术水平区分种植者。每个集群需要至少20次访谈。参照农场的最低数量为5个中的20个。参照农场的最佳数量为20个中的10个（平衡样本）。
C. 选择程序
受访者通过“配额随机抽样”程序随机选择。首先随机选择种植者，然后检查他们是否符合作物、地区、农场规模等配额。为了避免在单一抽样点集中大量访谈，访谈员被指示在一个村庄内最多进行5次访谈。
BF从埃及筛选出的受访者来自阿斯尤特、米尼亚、班伊苏夫、沙尔基亚、伊兹马利亚，根据以下标准进行选择：
- 背景：露天番茄
- 水灌溉
- 使用拖拉机或手工（例如，使用锄头）耕作
- 使用化学和/或有机肥料
- 销售收获是收获后的主要活动
数据收集方式
---------------------------
面对面 [f2f]
研究工具
---------------------------
2019年的数据收集工具涵盖了以下信息：
(A) 收割前信息
第一部分：筛选
第二部分：联系方式
第三部分：农场特征
a. 生物多样性保护
b. 土壤保护
c. 土壤侵蚀
d. 种植区域描述
e. 作物栽培和安全措施培训
第四部分：收割前耕作实践
a. 播种和果实发育 - 场地作物
b. 播种和果实发育 - 树木作物
c. 播种和果实发育 - 甘蔗
d. 播种和果实发育 - 花椰菜
e. 种子处理
(B) 收割信息
第五部分：收割后耕作实践
a. 肥料使用
b. 作物保护产品
c. 每种作物的收割时间和质量 - 场地作物
d. 每种作物的收割时间和质量 - 树木作物
e. 每种作物的收割时间和质量 - 甘蔗
f. 每种作物的收割时间和质量 - 香蕉
g. 收割后
第六部分 - 收割后其他投入
a. 投入成本
b. 非生物胁迫
c. 灌溉