Challenges and opportunities of tablet-based electronic performance data collection and feedback system for artificial insemination delivery in dairy cattle: experience from the land O’Lakes Venture37 PAID project in Ethiopia

Abstract Land O’Lakes venture37 (v37) has been using Open Data Kit (ODK) to collect Artificial Insemination (AI) technicians’ field performance within the Public Private Partnership for Artificial Insemination Delivery (PAID) Ethiopia Project from Amhara, Oromia, Southern Nations Nationalities Peoples (SNNP), and Tigray regions. This study assesses the challenges and opportunities of the Tablet-based project field performance data collection and feedback system. Information gathered from AI technicians using a structured questionnaire was analyzed using the crosstabulation procedures of SPSS and index methods. Almost all (96.7%) the interviewees had never used Tablet for data collection before PAID project. After training, about 82% of the respondents were able to use the ODK tool to collect and send data to PAID. However, there were reports on the challenges of using ODK that include poor or interruption of internet connectivity (index = 0.272), lengthy (time taking) nature of the forms (index = 0.241), and difficulty of updating the system (index = 0.093). Almost all the respondents were willing to use ODK in the future, provided internet is available and reliable (index = 0.248), and proper training is given (index = 0.238) for using ODK provides confidence that the report is delivered (index = 0.259), and it is timesaving (index = 0.24).


PUBLIC INTEREST STATEMENT
Field data can be collected using paper and/or electronically using tools like ODK.Artificial insemination (AI) technicians in Amhara, Oromia and SNNP regions who were trained to use ODK to collect data about Artificial Insemination Delivery in Dairy Cattle by Land O'Lakes Venture37 PAID project in Ethiopia were asked to provide their view on the challenges and opportunities of the Tablet-based data collection and feedback system.The result revealed that training about Tablet and ODK has helped AI technicians to use the technology to collect data and send it to PAID project.However, some of the respondents reported that poor or interruption of internet connectivity, the lengthy (time taking) nature of the forms, and difficulty in updating the system were the challenges of the system.Provided all the challenges are solved, AI technicians were willing to use ODK for future data collection.
Almost all the respondents were willing to use ODK in the future, provided internet is available and reliable (index = 0.248), and proper training is given (index = 0.238) for using ODK provides confidence that the report is delivered (index = 0.259), and it is timesaving (index = 0.24).

Introduction
Most data collection in developing countries especially in the agricultural research and development sector is paper-based.However, increasing demands for quality statistical data and the need for their accessibility in real-time have caused the development of new modern technologies, especially in the data collection process.One of the solutions for accelerating the speed, accuracy, and accessibility of data is the use of modern IT instruments in data collection (Fitzgerald and FitzGibbon, 2014;King et al., 2014).
Open Data Kit (ODK) is an open-source software for collecting, managing and using data in resource-constrained environments.ODK is an extensible, open-source suite of tools designed to facilitate tasks at every level of data collection campaigns.It allows offline data collection using mobile devices in remote areas.The collected data can be submitted to a server when internet connectivity is available.It allows communities to aggregate data with full control over the collected data and the servers where the data are stored (JeffreyCoker et al., 2010; Terre des Hommes, CartONG and UNHCR, 2017); https://en.wikipedia.org/wiki/ODK_software.ODK helps millions of people collect data quickly, accurately, offline, and at scale.It is the standard for offline data collection and is trusted by organizations such as the World Health Organization, Red Cross, Carter Center, CGIAR, Google, and many more (https://opendatakit.org/).The ILRI African Dairy Genetic Gains (ADGG) together with the National Animal Genetic Improvement Institute (NAGII) has established the ODK data collection system for dairy producers in Ethiopia to collect, aggregate and send electronic data directly to the main ADGG/NAGII data Platform on which data from different farm types (smallholders, medium-scale, and large-scale) is collected and stored (Oyieng et al., 2020).
The Public-Private Partnership for Artificial Insemination Delivery (PAID) Project, implemented by the Land O'Lakes Venture 37, is a project to address genetic constraints to dairy productivity growth in Ethiopia by strengthening Artificial Insemination (AI) delivery through public-private partnerships (PPPs).To bring about the intended dairy genetic gain in specific geographic coverage and time frame through an efficient AI service delivery system, a reliable supply of quality semen and liquid nitrogen (LN2) are prerequisites.With the intention of contributing to the country's dairy genetic gain endeavors through efficient AI service delivery, Land O'Lakes Venture 37 (LOL V37) has been applying ODK tool to collect field performance data from the project-supported AI technicians in Amhara, Oromia, Southern Nations Nationalities Peoples (SNNP; currently, SNNP include Sidama and South-west Ethiopia regions), and Tigray regions.Efficient AI delivery and related performance data recording are critical for successful crossbreeding in dairy cattle and the sustainability of a profitable dairy business.
Understanding the effectiveness of the Tablet-based Data collection and feedback system and related challenges and opportunities as well as establishing a reliable data collection system that can be applied at all levels is required.The objective of this study, therefore, was to assess the effectiveness, challenges, and opportunities of Tablet-based Data Collection and Feedback System in the AI Delivery system in Amhara, SNNP, and Oromia regions of Ethiopia.

Materials and methods
A structured questionnaire was developed and employed to collect data on the use of the ODK tool to collect and report data on Artificial Insemination (AI) services provided by PAID project supported AI technicians in 2021.The data was collected from AI technicians trained on the use of ODK to collect AI service delivery-related field data in Amhara, SNNP, and Oromia regions and send to the PAID project.Of the total 621 project-supported AI technicians, 151 (50 from Amhara, 50 from SNNP, and 51 from Oromia) were randomly selected and interviewed through calls over the phone.The content of the interview included information about prior knowledge of the respondents on the use of IT tools; the current use of the ODK tool; the support provided to the AI technicians by regional coordinators and PAID staff; and the opportunities and challenges of ODK tool.Prior to administering the survey tool, interviewees were briefed about the objective of the assessment.
The data was encoded into SPSS software and analyzed using descriptive statistics (crosstabs) procedures (Jinn, 2011).The Crosstabs procedure includes the Mantel-Haenszel test of trend among its chi-square test statistics.This test was calculated as: Chi-Square (MH) = (W-1)*r 2 Where: W = the sum of the case weights and r 2 is the squared Pearson correlation between the row and column variables.
Those parameters with multiple responses and ratings were analyzed using index calculation using the formula provided by Kosgey (2004):

General information of the respondents
While all the 151 sample respondent AI technicians were from the public sector, most (84.1%) of them were men (Table 1).There was not any association (p = 0.056) observed between the regions where the AI technicians are attached and their sex.The association analysis between the sex of the respondent AI technicians and data sending using ODK tool reveals a significant association (p = 0.000) where 87.4% of men respondents were able to use ODK, while only 54.2% of women AI technicians used ODK for field performance data collection and sending (Table 2).All the interviewees received either refreshments or full-fledged training in AI techniques by the PAID project staff.

Prior experiences of AI technicians on the use of Smart Phone and Tablet
As depicted in Table 3, most (61.6%) of the AI technicians had prior experience in using a smartphone.There was a significant (p = 0.00) association between region and prior experience in the use of a smartphone that 84.3% of the sample respondents from Oromia and 60% from SNNP regions reportedly had pre-PAID project experience in using a smartphone.About 60% of the respondent in the Amhara region, on the other hand, did not have prior experience.Overall, while 80.1% of the respondent AI technicians from all the regions had no experience of using a Tablet for any purpose, 96.7% of them reported to have never used Tablet for such field performance data collection and communication purposes prior to PAID Project.As revealed by the chi-square test, no apparent association was observed between prior experience in the use of smartphones (p = 0.113) and Tablet (p = 0.28) for field performance data collection and sending using the ODK tool (Annex Table 2 and Annex Table 3).

Information about the Tablet
All the respondents have received a Tablet from the PAID project of which 68.9% were without SIM card slot, with the difference (31.1%) having SIM card slot (Table 4).Overall, most (68.7%) of the Tablets provided to AI technicians are still functional.Among the Tablets that are not functional (31.3%), most of them are broken (53.2%), handed to others (19.1%), and have Software issues (14.9%).Only a few numbers of the respondents (7.3%) did not have the Table at hand.

Training on ODK by PAID
All the respondents in the three regions reported to have taken the ODK training by PAID project staff at least once (55.6%) and at most four times (7.9%) (Table 5).There was a significant association (p = 0.00) between the regions and training frequency where most (96.0%)AI technicians from the Amhara region reportedly took the training only once while a quite a few numbers of AI technicians in Oromia (41.2) and SNNPR (38.0) regions took the training twice.This might be attributed to the remoteness of the Amhara region from Addis Ababa, where the PAID Ethiopia project coordination team is based.Most of the respondent AI technicians rated the training given by the PAID project team as Satisfied (46.4%) and Agreed (66.9%) that training material and manuals prepared on the ODK system are helpful to understand the system and facilitate the process of data capturing and sending.The training material prepared was rated as important (54.7%).

Use of ODK for data collection and sending
As summarized in Table 6, following a training, 82.1% of the respondents reported to have managed to send their respective field performance data using the ODK tool to the PAID project team at least once a month (54.8%), and four times a month (15.3%), once in a while (13.7), twice a month (10.5%), and every day (5.6%).The remaining about 18% of the respondents did not use ODK either to collect or send data to PAID due to internet connectivity issues (Index = 0.509) and they do not understand the system (Index = 0.205) (Table 6).This implies that data collectors need to get the required training until they internalize the system as a few numbers of the respondents   (19%) rated the system as difficult.About 38% of all the respondents rated using ODK as Easy (38.4%) followed by Very easy (21.9%).
In addition, AI technicians need to get the required support and follow-up in the ODK system from regional cluster coordinators and PAID project staff.There were quite a few AI technicians who reported to have not received the required support from the regional cluster coordinators (30%) and PAID project staff (18%) (Table 7 and 8).There was a significant association between the regions and the support from the regional coordinators (p = 0.01), and PAID project staff (p = 0.00).More than 55% of AI technicians from Oromia regions stated that they are either neutral or disagree with the support from the PAID project staff.However, though there were skill and knowledge differences among regional coordinators and budget and logistic problems, information gathered from both regional coordinators and PAID staff revealed that the required support was given to AI technicians.Despite all the possession and functionality of the tablets, training on ODK, and easiness of the system, none (100%) of the respondents are currently using the ODK tool for data sending to PAID project.Overall, the three priority reasons (sum index = 0.679) of the respondent AI technicians for not using the ODK tool for data collection and sending are lack of internet access (index = 0.281), the project is not receiving ODK data (index = 0.214), and difficulty of updating the list in the system (index = 0.184) (Table 7).
Information obtained from the project revealed that repeated efforts were undertaken, and a significant budget was allocated to use the ODK system as a sole source of performance data collection and sending.However, the volume of data received through the system was not justifiable to use as the sole source of the data collection system.On the other hand, the system was administered from Nairobi, Kenya and this has been a bottleneck to fix system-related challenges at the right time.

WiFi for data sharing
About 71% of the respondents received WiFi Router from PAID project of which 46.7% took themselves while the rest (53.3%) was taken by the respondents friends (Table 9).Most of the respondents (92.0%) who took the WiFi themselves reportedly have the router in their hands.However, only 40.0% of them have used WiFi to send data to PAID project four times (52.5%), three times (17.5%),once (15%), and twice (15%).There was a positive association (p = 0.00) between the regions and router possession that most from the Amhara region (58.0%) did not get a router from the project.

Challenges of using ODK tool for data collection and sending
The three most important challenges (sum index of 0.606) of using the ODK tool to collect and send data to PAID project as reported by the respondents include lack of internet service (index = 0.272), the forms in the system being too long to complete (index = 0.241), and the difficulty of updating an older version of ODK (index = 0.093), among the list of challenges reported (Table 10).
Despite the list of challenges reported, almost all the respondents are willing (96.7%) to use ODK as a data collection and sending tool if the challenges are solved, and choose ODK (88.1%) as a future method of data collection (Table 11).Therefore, working on these three challenges can improve the problem by more than 60%.
The reasons forwarded for the choice of ODK system by the respondents include confidence on the timely delivery of the report (index = 0.259), time efficient (index = 0.240), and ease and convenience of use (index = 0.133) (Table 12).Whereas only 3% of the respondents reported the paper-based data collection methods to be easier and more convenient (index = 0.310), timesaving (index = 0.279), and energy-saving (index = 0.159).There are earlier research results that   reported IT-based data collection methods to be cost-effective, timely and accurate compared with that of paper-based one (Fitzgerald and FitzGibbon, 2014;Erin Satterlee et al., 2015).

Solutions to re-start the Tablet-based data collection system
To restart the use of ODK as a data collection and sending tool, a list of activities is generated and prioritized by the respondent AI technicians (Table 13).Among the solutions forwarded, availing internet system/service to all AI technicians (index = 0.248), providing proper training to AI technicians about ODK (index = 0.238), and changing those tablets without SIM card slots to those with SIM card slots (index = 0.203) were the three very important ones accounting about 70% of all the activities forwarded.With respect to internet service, however, the project has provided WiFi router for about 70% of the respondents.Moreover, there was no significant association (p = 0.52) between receiving Tablet with SIM card slot and data sent to PAID project (Annex Table 1).It is pointed out that, however, improvements in telecommunications infrastructure may provide a more enabling environment for the use of ICTs in research in Ethiopia (Fitzgerald and FitzGibbon, 2014).

Conclusions and Recommendations
All the AI technicians interviewed were public; therefore, including those private AI technicians could have added value to the data collection process and adoption of the ODK technology in the future.The most important challenges of using ODK were internet problem, and understanding the system.Therefore, availing internet by changing those tablets without Sim card slots with those with SIM card slots and proper training, and supporting AI technicians are important to use ODK as future data collection means.All the respondents are willing to use ODK provided the challenges are solved to which most of them rated the use of ODK easy.This is because ODK provides confidence for data delivery, saves time and it is easy and convenient.Finally, to collect accurate and transfer and share data, the ODK data collection system should be applied by providing proper training and availing internet access to data collectors.