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Crop Cutting Yield Survey for the 2019 Major Season in GIC Project Areas

The “Green Innovation Centres for the Agricultural and Food Sector (GIC)-GIZ”, a special initiative commissioned by the German Federal Ministry for Economic Cooperation and Development (BMZ), as part the Ghana country package of the global programme, supports the Ministry of Food and Agriculture (MoFA) and selected actors in the value chains (VCs) of maize and rice. GIC Ghana officially started in April, 2015, and still running till March, 2021. The Project is being implemented by the Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ) GmbH which focuses on the improvement of livelihood of smallholder farmers and other stakeholders in these value chains in the Ghanaian regions of Volta, Oti Eastern, Ashanti, Ahafo Bono and Bono East.

This recount describes the methodological approach that was used in the assessment of yield for maize and rice in selected communities and districts in Ghana for the Green Innovation Centre for the Agriculture and Food Sector – Ghana (GIC). The field work lasted from April to October 2019. This period corresponded to the beginning of the growing to the end of the harvesting season for the major season in the selected regions. Two major cereal crops were chosen: Maize and Rice: because they are among the most important basic staple foods in Ghana.

It is widely known that agricultural productivity is important to the overall economy. It is a key driver for the wellbeing of farmers, the agro-based industry and the general economy. It is linked to food security food prices and poverty alleviation in developing countries. In addition, food supplies have to be geared towards meeting the challenges of increasing global population, changes in income and the resultant changes in diet. Hence research on agricultural productivity is of paramount importance.

The use of data and technology in agriculture has far reaching impact on agri-businesses, and enables a more efficient and accurate decision making throughout the cultivation cycle. The smart approach towards Crop Cutting Exercise (CCE) are beneficial on multiple levels. Crop cutting is the widest spread method of yield estimation used through the world. The techniques were developed in India in 1940s and 1950s. Crop cutting experiments are regularly conducted in order to obtain fair, precise and accurate estimate of yield of principal crops which include rice, maize, cassava, groundnut, plantain, mango, cocoa, sorghum, millet and soybean. These experiments are conducted through stratified random sampling technique taking block as a primary unit of planning by the agricultural development unit in each district.

Methodology and Design

The training was scheduled to start from the 1st - 5th April, 2019, at the Bunso Cocoa College for the data collection enumerators (40 enumerators from 20 Districts in the Volta, Eastern, Ashanti, Bono, Oti, Bono East Regions). Enumerators invited were mostly experienced officers who had extensive experience in conducting objective crop cut. This was to allow participants to provide relevant feedback to the facilitating team regarding the content and structure of the forms and review especially to the study crops (maize and rice).

During the training, participants were taken through the training program: (i) Field area measurement procedure and how to conduct yield studies; (ii) Teaching on the use of the programmable calculators, GPS technology, and the survey solution interview application. Throughout the training, three crop cutting survey instruments, FORM A, B and C were each presented in details and discussed with the participants. Additionally, field area measurement and establishing the yield plots were presented, discussed and demonstrated to participants. The forms were also pilot tested in a locality around the Bunso Cocoa College. Finally, participants were taken through the synchronization of collected CAPI data from the enumerator to the supervisor and validation of data by supervisors to the sever.

In each district, a team was made up of an enumerator and a supervisor. Each team collected data from 5 selected maize/ rice farmers. A total of 100 farms were targeted in the major season crop cutting survey. However, 107 respondents were involved (89 maize and 18 rice farmers). The overall field exercise was coordinated by officers of ASCU and the GIC team providing support. The field data collection activities in the district were preceded by an identification exercise as well as forewarning these selected holders of the exercise and giving solution to their complains.

Field area measurement and yield studies using the tape, GPS and programmable calculator were undertaken. A CAPI practice using interview application to collect Field results from farm area measurement and yield studies was also practiced. Participants were also given an overview of survey solutions application CAPI.

Participants testing CAPI on tablet

Bunso Cocoa College (training center)

Data Collection Method (Selection of Farmers)

Ten farmers were randomly selected per district for the survey. Since the survey required a sample size of five (5) farmers per district, the first five selected were originally earmarked for the yield studies and the remaining five kept as reserve. The sample frame of farmers was provided by the GIZ for each of the twenty (20) districts. Microsoft Excel was the application platform used to undertake the sampling procedure.
The random sampling, as mentioned earlier was done for each district, following the procedure as listed below:
• Sampling ratio was obtained by finding the ratio between the total number of farmers in the frame for each district and the sample size.
• A random starting point for the selection was determined between the first name on the list and a sampling space equal to the size of the sampling ratio.
• To obtain a reasonable representation across the communities and the required relative male and female representation the frame for each district was sorted by community and sex.
• From the randomly selected first farmer on the ordered list the remaining nine were selected at intervals of the sampling ratio calculated.

Main Findings

*A total of 5 farmers were selected per district for this study. Farm area measurement and crop cutting exercise was undertaken on 89 maize farms and 18 rice farms in the Atiwa, Kwaebibirem and Lower Manya Krobo districts.

*The average yield for Rice and maize was 4.54Mt/Ha and 2.75Mt/Ha respectively.

*The total output for maize was 199.27Mt whilst that for rice was 64.8Mt.

Discussion

The data gathered from this study is generalized to the entire region and provides a fairly accurate assessment of the average yield of the district or region under study. The data gathered from CCE is useful to multiple stakeholders in the agricultural value chain.

Whereas government use it for planning agricultural policies and programs for the future, the information helps financial institutions with all the inputs they need before offering loans or insurance coverage if there is a poor harvest or crop failure. Plus, the help of these scientific methods, the government can improve overall efficiency by utilizing their resources in the best possible way.

Importance of the Survey

This survey conducted between GIC and SRID will enhance the crop productivity monitoring capacity of MoFA agricultural extension agents in GIC selected interventions districts in the Ashanti, Brong Ahafo, Eastern and Volta Regions by the Agricultural Statistics and census unit, Statistics, Research and Information Directorate of the Ministry of Food and Agriculture.

The training carried out by ASCU will strengthen the capacity of extension agents and obtain representative yield data of smallholder maize and rice farmers via crop cutting survey.

The presented methodological design of the study, the organization of the data collection activities, the calculation procedures, the use of CAPI and the main results from the field can be adopted in other related researches.

Also, the data collected will greatly enhance GIC's ability to better evaluate/complement existing data, and to inform the project’s key indicator related to yield increase. In a similar way data will be made available for the further surveys of SRID.