Good Growth Plan 2014-2019 - Ukraine

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 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
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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. 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 Ukraine were selected based on the following criterion:

(a) smallholder maize growers
Grain corn
Region: Cherkassy & Kiev

(b) smallholder sunflower growers
Region: Vinnitsa, Kiev & Cherkassy

Mode of data collection
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Face-to-face [f2f]

Research instrument
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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
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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
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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
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先正达致力于提升作物生产效率，并更加高效地利用有限的资源，如土地、水资源和投入品。自2014年起，先正达在全球真实农场网络中测量了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，涵盖46个国家中超过20种不同的作物。数据（除美国数据和英国、德国、波兰、捷克共和国、法国和西班牙的裸麦数据外）由独立的市场研究机构Kynetec（原Market Probe）收集、整合和报告。这些数据可作为作物产量和投入效率的基准。
地理覆盖范围
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全国覆盖
分析单元
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农业经营单位
数据类型
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样本调查数据 [ssd]
抽样程序
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A. 样本设计
农场被分为代表在具有同质化农业生态条件的区域内种植同种作物的簇群，包括可比类型的农场。样本包括参照农场和基准农场。参照农场由先正达选定，而基准农场由Kynetec在同一簇群内随机选定。
B. 样本量
每个簇群的样本量是根据旨在测量作物效率随时间统计显著增加的目标来确定的。这是基于目标生产率增加和关于每个簇群内农场指标变异性的假设由Kynetec完成的。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的差异。簇群内的变异性基于公开研究和专家意见。此外，种植者也被分为簇群，作为控制方差的一种手段，以及在作物规模、地区和技术水平方面区分种植者。每个簇群需要至少20次访谈。最小参照农场数量为5个中的20个。最优参照农场数量为20个中的10个（平衡样本）。
C. 选择程序
受访者通过基于配额的随机抽样程序随机选取。首先随机选取种植者，然后检查他们是否符合作物、地区、农场规模等配额。为了避免在单个抽样点集中进行大量访谈，访谈者被指示在一个村庄内最多进行5次访谈。
BF从乌克兰筛选出的样本是基于以下标准选定的：
(a) 小型玉米种植者
谷物玉米
地区：切尔卡瑟和基辅
(b) 小型向日葵种植者
地区：文尼察、基辅和切尔卡瑟
数据收集方式
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面对面 [f2f]
研究工具
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2019年的数据收集工具涵盖了以下信息：
(A) 丰收前信息
第一部分：筛选
第二部分：联系信息
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. 种植区描述
e. 作物栽培和安全措施培训
第四部分：丰收前农业实践
a. 播种和果实发育 - 田地作物
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) 丰收信息
第五部分：丰收后农业实践
a. 肥料使用
b. 作物保护产品
c. 每种作物的收获时间和质量 - 田地作物
d. 每种作物的收获时间和质量 - 树木作物
e. 每种作物的收获时间和质量 - 甘蔗
f. 每种作物的收获时间和质量 - 香蕉
g. 收获后
第六部分 - 丰收后其他投入
a. 投入成本
b. 非生物胁迫
c. 灌溉
查看所有问卷在外部材料标签页。
数据清洗操作
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数据处理：
Kynetec使用SPSS（社会科学统计软件包）进行数据录入、清洗、分析和报告。在收集后，农场数据被录入到本地数据库中，由当地的Kynetec机构进行审查和质量检查。在出现缺失值或不一致的情况下，会重新联系农民。在某些情况下，通过当地专家（例如，零售商）验证种植者数据，以确保数据准确性和有效性。在国家层面清洗后，农场级数据提交给全球Kynetec总部进行处理。在出现缺失值或不一致的情况下，当地的Kynetec办公室会重新联系以澄清和解决问题。
质量保证
在整个数据收集和报告过程中，实施了各种一致性检查和内部控制，以确保数据无偏见、高质量。
• 筛选：每个种植者都根据簇群特定标准由Kynetec筛选和选定，以确保每个簇群内种植者群体的可比性。这有助于保持变异性低。
• 问卷评估：问卷符合项目的全球目标，并适应本地环境（例如，访谈者和种植者应理解所问内容）。每年根据几个标准评估问卷，并在必要时进行更新。
• 访谈者简报：每年，熟悉本地农业环境的当地访谈者都会进行彻底的简报，以便充分理解问卷，从而从受访者那里获得无偏见、准确的答案。
• 答案交叉验证：
o Kynetec通过数字数据录入工具捕获所有种植者的回答。在此工具中自动化了各种逻辑和一致性检查（例如，公顷总数不能大于农场规模）
o Kynetec通过以下三种方式交叉验证种植者的答案：
1. 在种植者内部（检查种植者在访谈期间是否作出一致的回答）
2. 横跨年份（检查种植者在多年间的回答是否一致）
3. 在簇群内部（比较一个种植者的回答与同一组其他人的回答）
o 所有上述不一致性都会通过联系种植者并要求他们核实其答案来跟进。核实后更新数据。所有更新都会被跟踪。
• 检查和讨论演变和模式：Kynetec和先正达每月共同计算、讨论和审查全球演变。
• 敏感性分析：进行敏感性分析以评估全球结果，包括异常值、保留率和整体统计稳健性。敏感性分析的结果由Kynetec和先正达共同讨论。
• 建议对使用位置数据集中行政级别1变量的用户，在使用此变量时需谨慎，并与邮政编码变量进行交叉核对。
数据评估
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由于上述检查，发现了肥料使用数据中的不规则性，必须对其进行纠正：
对于2014年数据收集波次，受访者被要求提供在田间应用的肥料NPK比率的总估计值。从2015年起，问卷被重新设计以更加精确，并按单个肥料产品获取数据。测量肥料投入的新方法导致结果更准确，但也使得年度间比较变得困难。在评估了几个解决方案后，通过计算以下年份肥料使用量的加权平均值重新估计了2014年的肥料使用量（NPK投入）。