Good Growth Plan 2014-2016 - Costa Rica

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.

Screening of Costa Rica BF:
Cariari (Pococí cantón), Guápiles cantón, Matina cantón, Siquirres cantón and Sarapiquí cantón

Low:
- Productivity: between 1800 and 3200 boxes/ha/year (approved by Syngenta, original cut-off: < 2350 boxes/ha/year)
- Generic CP use: use mostly generic products (use mix of generic products and branded products)

Background info:
- reduced access to market
- disease affection: Sigatoka: Sigatoka disease is already present in all banana farms in Costa Rica; it is an endemic disease in Latin America region

Banana growers in the province of Limon

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产量，并更有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4000个农场，涵盖46个国家中的20多种作物。数据（除美国数据以及英国、德国、波兰、捷克共和国、法国和西班牙的燕麦数据外）由Kynetec（前身为Market Probe）收集、整合并报告，该机构是一家独立的市场研究机构。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
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全国覆盖
分析单元
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农业经营
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
农场被分组成集群，这些集群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。样本包括参考农场和基准农场。参考农场由先正达选择，基准农场由Kynetec在相同集群内随机选择。
B. 样本量
每个集群的样本量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。这是由Kynetec根据目标生产率增加和每个集群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。集群内的变异性基于公开研究和专家意见。此外，种植者也被分组成集群，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。每个集群需要至少20次访谈。最少有5家20家的参考农场。最佳参考农场数量为10家20家（平衡样本）。
C. 选择程序
受访者通过“配额随机抽样”程序随机挑选。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。为了避免在一个抽样点集中大量访谈，访谈员被指示在一个村庄最多进行5次访谈。
哥斯达黎加BF的筛选：
Cariari（Pococí cantón）、Guápiles cantón、Matina cantón、Siquirres cantón和Sarapiquí cantón
低：
- 产量：介于1800至3200箱/公顷/年之间（经先正达批准，原始截止点：< 2350箱/公顷/年）
- 通用品CP使用：主要使用通用品（使用通用品和品牌产品的混合物）
背景信息：
- 市场准入减少
- 病害影响：Sigatoka：Sigatoka病已在哥斯达黎加的所有香蕉农场中存在；它是拉丁美洲地区的 endemic disease
省立里蒙的香蕉种植者
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) 采收前信息
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. 种植区域描述
e. 作物栽培和安全措施培训
第四部分：采收前耕作实践
a. 种植和果实发育 - 田地作物
b. 种植和果实发育 - 树木作物
c. 种植和果实发育 - 甘蔗
d. 种植和果实发育 - 花菜
e. 种子处理
(B) 采收信息
第五部分：采收后耕作实践
a. 肥料使用
b. 作物保护产品
c. 采收时间与每作物质量 - 田地作物
d. 采收时间与每作物质量 - 树木作物
e. 采收时间与每作物质量 - 甘蔗
f. 采收时间与每作物质量 - 香蕉
g. 采收后
第六部分 - 采收后其他投入
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选项卡中
数据处理
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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。收集后，农场数据被输入到本地数据库中，由当地Kynetec机构进行审查和质量检查。在缺失值或不一致的情况下，会重新联系农民。在某些情况下，种植者数据会与当地专家（例如零售商）进行核实，以确保数据的准确性和有效性。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。在缺失值或不一致的情况下，当地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，以确保数据的客观性和高质量。
• 筛选：每个种植者都是根据集群特定的标准由Kynetec筛选和选择的，以确保每个集群中都有一个可比较的种植者群体。这有助于保持变异性低。
• 问卷评估：问卷与项目的全球目标一致，并根据当地环境（例如，访谈者和种植者应理解所问内容）进行调整。每年根据几个标准评估问卷，并在必要时进行更新。
• 访谈员简报：每年，熟悉当地农业环境的当地访谈员都会进行彻底简报，以确保充分理解问卷，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捉所有种植者的响应。在此工具中自动执行各种逻辑和一致性检查（例如，公顷总作物面积不能大于农场面积）
o Kynetec以三种不同的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做出一致的回答）
2. 横跨年份（检查种植者在多年间是否做出一致的回答）
3. 在集群内部（将种植者的回答与组内其他人的回答进行比较）
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。
• 建议对有兴趣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检查。
数据评估
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由于上述检查，发现了肥料使用数据的不规则性，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野中应用的肥料NPK率的总体估计值。从2015年起，问卷被重新设计，以更精确地获取按个别肥料产品获取的数据。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。在评估了多种解决方案后，通过计算后续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输入）。