Ensuring sustainable consumption and production patterns by 2030 was formulated by the United Nations member states as one of the 17 Sustainable Development Goals [1
]. A sustainable and economically viable milk production can be achieved by the demand-based feeding of cows. Although pasture-based systems score better on ecological and economic sustainability than indoor dairy production systems [2
], it is more difficult to implement demand-based feeding in pasture-based systems because the growth and nutritional value of herbage is affected by many environmental factors [3
In pasture-based systems, herbage intake, not only in terms of quantity but also in terms of quality, is difficult to estimate, making it difficult to control how much concentrate feed to add. Well-implemented pasture management in strip or rotational grazing systems can ensure the high production levels and nutritional health of dairy cows [4
]. Moreover, it can combine the farmer’s production goals with multiple ecosystem services [6
] and positive aspects of animal welfare [9
] that pasture-based systems offer.
Demand-based feeding on pasture relies on good pasture management, and thus experience is needed [8
]. However, there are tools to support pasture management. For example, farmers can take destructive samples of defined grassland areas to weigh and measure available herbage mass [10
], or they can use rising plate meters (RPMs) to convert measurements of compressed sward height into an estimate of herbage mass [11
]. These estimates can be visualized in a so-called “grass wedge”, i.e., a bar graph where paddocks are sorted by the amount of available herbage so that it is easy to see which paddocks are below and which are above the required amount of feed for the grazing herd. Based on the grass wedge, farmers can better schedule the allocating or harvesting of paddocks, as well as anticipate herbage shortages [12
]. Additionally, farmers can send dried herbage samples to a laboratory or use tables to look up the nutritional value of fresh herbage in the fields [13
]. With this, farmers can manage the supplementary use of concentrates more efficiently [2
However, even the basic tasks of grazing, such as setting up and checking fences and water troughs or bringing animals to grazing areas and back, are perceived as labor intensive [14
]. Even more work is added when the precise herbage measurements are to be conducted [16
]. There is even more time pressure during bad weather periods because the grass must not be wet for some measurement methods, which additionally complicates the completion of the task. The additional work can affect social components of sustainable farming practice, because there is less time and flexibility for family and social life [17
In general, the volume of work in dairy farming and its physical strain are high [18
], as is the psychological workload for farm managers [20
]. Therefore, some farmers identified an unsatisfactory quality of life in a Swiss study [21
]. In order for dairy farming to remain an attractive occupation for future generations, a better work–life balance is urgently needed [22
Automation attempts to address these aspects of unsatisfactory social sustainability on farms, particularly by reducing working hours and physical workloads in standardized production processes [18
]. Thus, new approaches and tools are constantly emerging from research and industry that are semi-automated and make estimations nearly in real-time and on-site, which supports farmers’ decision-making. They are hereafter referred to as smart farming tools.
Smart farming tools are being developed to assist herbage measurements in order to make paddock management more precise. One tool, which is already in use, is a semi-automated RPM that converts a sward height measurement into a herbage mass estimate in real-time [26
]. Another tool is an unmanned aerial vehicle (UAV) that is equipped with optical sensors and generates a color-scaled farm map to indicate the spatially available herbage masses. This tool is still in the development stage, because an established prediction model was not accurate enough for practical use by farmers in an evaluation study [27
]. However, it is seen as having a large potential to replace RPM measurements because it has a high operative capability in determining herbage mass and is also affordable [28
]. In a study by Lussem et al. [30
], the UAV approach used even outperformed an RPM in measurement accuracy. However, Sishodia et al. [31
] note that image processing is complex and needs expertise. Therefore, it is difficult to develop an easy-to-use workflow for real-time application to promote the adoption of the tool [31
]. A third tool uses near-infrared reflectance spectroscopy (NIRS) to analyze the nutritive value of fresh herbage cuttings on-site. By weighing the cut herbage samples, they can also be used to estimate the amount of herbage available in pastures and to create the so called grass wedge [32
]. The NIRS is commercialized and available as a mobile work station to be operated either in farm offices or car boots or as fixed variant mounted onto harvest machinery. Calibrations are constantly evolving and are updated from time to time by the manufacturer.
To the best of our knowledge, labor times are as yet unknown for these tools, and therefore also the implications for social sustainability on farms. There are different methods to quantify or estimate labor time requirements. Interviewing persons can provide estimations on labor input. A more precise method is a work diary, for example, via a smartphone app as used by Deming et al. [16
]. Although this method is partly subjective, it is well suited to getting insight into the required labor input of a production system. Thus, the total workload on a farm or the relative differences in workload for different work operations can be estimated [33
However, work operations can also be directly observed and measured using traditional watches, hand-held computers, or smart devices with time measurement apps [34
]. Indirect time measurements are possible by taking videos of working persons and analyzing video footage by means of time measurement software [36
]. Direct and indirect time measurements are usually no longer done at the farm level, but at the level of individual work operations and procedures, for example, harvesting forage with a rotary mower [39
] or trellising greenhouse crops with an angle of 30° [40
]. Hereby, it is essential to determine influencing factors, for example, the distance driven with a tractor or the number of pepper and tomato plants trellised within the measured time interval.
These time measurements, which are based on a variety of observations on different farms, can further be used to establish dynamic labor time models [41
]. Dynamic models allow estimating labor time requirements under changing conditions [38
], for example, large versus small farms. Therefore, they are extremely valuable for labor planning and for comparing the labor input of different work procedures before a potential purchase decision is made.
The present study focuses on three smart farming tools supporting paddock management decisions for which there is little or no information available about the temporal labor input. No work diaries or survey data were available for two of the tools investigated, because they were still relatively new and have not yet been widely applied in practical agriculture. In addition, the best-practice workflow on farms is unknown, and it has not yet been studied under which farm conditions, such as farm size, pasture subdivision, and spatial and botanical heterogeneity, the tools are best used.
To study the above-mentioned aspects, dynamic labor time models were established to determine the required labor time. Furthermore, work observations were conducted to measure time in each work procedure.
The objectives of this study were (i) to model the labor time requirement of three smart farming tools and the conventional approach for herbage measurement on an exemplary dairy farm and (ii) to identify potential time savings by optimizing the smart farming tool applications, especially their workflows. In addition (iii), the impact of the farm size and the subdivision of the milking platform on labor input was investigated.
3.1. Labor Input under Different Farm Scenarios
The labor time requirement for RPM and UAV increased most with increasing paddock size (Figure 2
). Enlarging paddocks had the greatest effect on the labor time requirement for the UAV, whereby the required time was disproportionately greater than with other tools from a paddock size of 4 ha (i.e., a milking platform of >80 ha). Each additional 9 ha to be sampled using the UAV required flying back to the home-point to change batteries. Below paddock sizes of 1 ha (i.e., a milking platform of <20 ha), the UAV required less labor time than the other tools.
An increase in paddock size affected the labor time required for direct observation and for NIRS slightly less, at least under the assumption that the number of sampled squares per paddock is constant. NIRS required more time than the RPM and direct observation among all paddock sizes.
When subdividing a given milking platform into fewer or more paddocks, the labor time requirement of the UAV tool remained approximately the same despite increasing paddock numbers (Figure 3
). In contrast, labor time requirements for the other two smart farming tools and the direct observation increased significantly as the number of paddocks increased. For NIRS and direct observation, the labor time requirement increased even more with the increasing number of paddocks than for the RPM, making them unfavorable for large numbers of paddocks. NIRS required always more time than direct observation. The RPM required the least time when used on >4 and <30 paddocks.
3.2. Influencing Factors of Labor Input
In contrast to the other tools, the UAV’s labor time requirement was hardly influenced by the number of herbage estimates and sampling points per paddock, the distances between the paddocks, or the rotation scheduling (as described in Table 1
), because in any case the UAV flies over and samples the entire milking platform. However, the size of the paddocks and the battery life do play a role for the labor input.
A strong effect of the number of sampling points per paddock on labor input was found for the NIRS tool (i.e., the number of cut samples). Additionally, the labor time requirement was noticeably influenced by the number of paddocks sampled in sequence and the fact that the sample bags must always be placed at the paddock boundaries owing to the limited carrying capacity of the user when no device with a carrying function (wheelbarrow or motorized cart) is used, as modeled in the present study. The labor time requirement reflects this particularly if herbage cuttings are repeated per paddock.
The work operation of cutting fresh herbage had a large share in the total labor time requirement of the NIRS tool and was responsible for the long adjustment and execution time in the field (see Section 3.3
). It included the time for walking the respective pattern within a paddock and the time for cutting the herbage sample in a representative area.
The labor time requirement rose with each additional sample that had been cut (Figure 4
). Whereas for up to three samples a diagonal was walked across the paddock, for four or more samples the complete cross was walked, as shown in Figure 1
. From four samples per paddock, the labor time requirement increased linearly by 1.23 manpower minutes (MPmin) with each additional sample.
3.3. Time Distribution on the Standardized Farm
The labor time requirement in measuring herbage by direct observation was 132.1 MPmin. In contrast, using the three smart farming tools required 116.1 MPmin (UAV), 182.2 MPmin (152.1 MPmin for cutting samples and 30.1 MPmin for NIRS analysis), and 94.2 MPmin (RPM) under the conditions of the standardized farm.
The work procedures direct observation, UAV, and RPM had almost no job preparation time on the farm. Nevertheless, for the direct observation and UAV, job closing time occurred on the farm to document and visualize the herbage measurement results (for the direct observation) or to download the image data and prepare them for image analysis (for the UAV). For the RPM, most of the work, starting from calibrating the plate until visualizing the measurements of herbage mass, was performed in the field. The RPM had no fault time due to work organization, in contrast to the other tools (Figure 5
In contrast to direct observation, the three smart farming tools required the user to spend a smaller proportion of time on transit. This is because the user of direct observation has to enter the paddocks to estimate herbage mass in order to get a picture of the complete area. In addition, the user has to go to the field a second time to evaluate the herbage quality for the number of paddocks that will be grazed next.
The two work operations of NIRS had the highest proportions of adjustment time and job closing time. Adjustment time occurred during cutting samples in the field when the user walked across paddocks and a representative area for sampling had to be found. Job closing time included work for the documentation and visualization of measurements.
The total operation time (t1) was smallest for UAV (55.1%), where the remaining 26.3% and 18.7% of the total required time were non-productive time (t3) and fault time (t2), respectively. Unlike the other tools, the UAV has a large proportion of supply time and job preparation time within the non-productive time.
3.4. Optimization Potential
An automation of the workflow of the UAV tool could reduce the total labor time requirement by 34.4 MPmin under the conditions of the standardized farm (Table 3
). Labor time invested in gathering multispectral images by means of UAV flights in the field could be reduced by 12.1 MPmin (−10.4%). Thereof, a great part are savings in supply time and job closing time (installing and uninstalling of UAV and supplies), and adjustment time (setting up the sensor calibration and capturing metrics, flight planning on the smart device). A minor part that can be saved is transit time (walking distance between the storage location of the UAV and the home-point).
During the workflow in the office, the total labor time requirement can be reduced by another 22.3 MPmin (−19.1%). This time saving is largely due to the reduction in job preparation and closing time if a wireless LAN connection were to be used for image data backup instead of a physical memory card, and if images were uploaded automatically to a database where they are stored and analyzed via an external service.
When collecting the cut samples for real-time NIRS analysis, a smartphone app for in-field use could reduce the total labor time requirement by 4.9 MPmin (−2.7%) (Table 3
). The app would eliminate the need to write down the bag weight in the field and enter it on the computer at the farm. In addition, the geolocation function of the smart device could automatically record the paddock name for the corresponding sample. A feed wedge would be available in real-time, and a decision on pasture management could be made directly in the field.
Another 5.7 MPmin (−18.8%) of job closing time spent on the farm could be saved by automating data storage, preparation, and visualization (−3.1% of the total labor time requirement). So far, the user manually exports the analysis results of the NIRS and creates an overview of the analyzed paddocks and the associated herbage quality values by means of table calculation software. Only then does the user derive the adequate feeding strategy. A user-friendly digital platform that clearly presents the analysis results on a farm map and communicates with the app would significantly reduce the time requirement for data storage, preparation, and visualization.
4.1. Studies on Labor Input
At the present time, scientific studies on labor time requirements of smart farming tools are rare. Reasons for the lack of scientific investigations may be the novelty of the smart farming tools, the little knowledge on best-practice workflows, and the lack of commercial implementation of the tools. Overall, the topic of precision grazing management has found little application in practice but is gaining more and more focus in agricultural research. Because the development of precision grazing systems is still in progress, there is even less research on the subject of labor input.
A study by Deming et al. [16
] determined a labor input of 0.23–0.35 h per cow and year for herbage measurements in Irish dairy farming. Nevertheless, there are no figures for the labor input involved in a one-time execution that could be compared with our results. In addition, it remains unclear which tools farmers used to measure herbage. Presumably, they cut samples or used the RPM, with both work procedures being promoted by the state agency [51
Two other studies have addressed the labor time requirements of the RPM and cutting fresh herbage samples [10
]. However, the estimated times of both studies were not based on time studies with a determination of generally valid values (in our case the standard times).
The time values for cutting samples in Lantinga et al. [10
] were collected under specific conditions during grassland and pasture experiments. They were not statistically validated and are therefore not generalizable to other conditions.
The time estimation from Murphy et al. [53
] is much lower (0.09 h/ha) than that of the present model (0.22 h/ha; one paddock of 1 ha). The authors calculated the labor time requirement for RPM measurements based on the average human walking pace and the manufacturer-recommended distance between measuring points. The work operations before and after measuring were not included. In the present study, when execution time is considered separately from other times before and after execution (i.e., only the work elements “walking with RPM on field” and “sampling RPM point”; Table A1
), the time estimate of 0.06 h/ha is close to that of Murphy et al. [53
In a follow-up study, Murphy et al. [54
] developed a model that calculates the ideal sampling route, in terms of time saving and measurement quality, for an unbiased sampling point selection. This new approach is useful when, owing to homogeneous pastures (spatially and botanically), few measurement points are sufficient to obtain a meaningful measurement result. In the present study, the number of measurement points was set to 45, regardless of the paddock size and homogeneity. However, fewer measurement points can be taken in small paddocks if pasture conditions permit, because reducing the number of measurement points without losing measurement quality is highly dependent on pasture heterogeneity.
4.2. Methodological Reflections
The chosen modeling approach, based on the REFA work element method, has great strengths. First, however, we would like to point out some weaknesses.
The sample size of the present study was sometimes small, owing to the novelty of the studied work procedures. Furthermore, some of the individual work element times depended on the response time of the user and on intellectual labor time. In these cases, the coefficient of variation and the epsilon value sometimes exceeded the desired maximum thresholds of 25% and 10% at maximum, respectively [37
]. However, the standard times of work elements that could be characterized by technical conditions showed very small variations (e.g., work element “HarvestLab analysis”; coefficient of variation: 11.72%; epsilon value: 3.11%). By measuring the work element times and creating the labor input models, it was nevertheless possible to gain informative insights for further development of the novel smart farming tools.
A great strength of the modeling approach used is the identification of the time saving potentials at the work element level. Thus, labor time requirements can be modeled for workplaces, conditions, or workflows that do not yet exist and used for planning purposes [36
Another advantage of the work element method and the labor time models established is the simulation of different farm conditions such as sizes, structures, and animal numbers [41
]. Thus, in the present study, it could be shown for which farm sizes and grazing managements (i.e., the duration of rotation cycles via the splitting of a given milking platform) each tool is most suitable.
4.3. Suitability of Tools for Use on Different Farms
One aspect that influences the suitability of a tool is the farm-specific conditions it is applied under. For example, Hart et al. [48
] have shown that the UAV is less suitable on a farm with widely distributed paddocks because of the long travelling distances and relatively short battery capacity. Therefore, it is more suitable for farms with compact milking platforms.
The heterogeneity of the grassland can also play a role in the suitability of a work procedure. Because the resolution of the UAV measurement is in the centimeter range and extends over the entire area of interest, spatial differences in herbage quantity and quality can potentially be detected very well. In contrast, NIRS measurement resolution is tied to the number of cut samples, and thus always a point sampling that either represents the area well or not. Because an increase in the number of cut samples increases the labor time requirement (Figure 4
), this work procedure is best suited for botanically and topographically homogeneous grasslands.
In the present study, we hypothesized that, despite increasing paddock size, a constant number of samples per paddock (two samples in the case of NIRS) was sufficient to make a statement about herbage mass and quality. Therefore, the increasing paddock size had hardly any influence on the labor time requirement for NIRS and direct observation (Figure 2
). Our assumption was based on a study by Nakagami [45
], who found that if it is possible to sample the areas with the most and the least herbage masses within a paddock, the herbage mass of the entire paddock can be estimated with an acceptable accuracy for practical farming. This requires a farmers’ experience and good knowledge of the spatial growth of grasslands as well as an elevated site to survey it [45
]. The issue here is that the measurement is likely to be inaccurate if the farm is botanically and topographically heterogeneous, especially with increasing paddock sizes, and therefore more samples should preferably be taken.
An alternative to the variant of NIRS modeled here would be to determine herbage mass using direct observation or the RPM, and randomly taking grab samples of fresh herbage while walking across the paddock for subsequent grass quality analysis with mobile NIRS. However, although using grab samples might save a certain amount of time compared with cutting samples, the problem of pasture heterogeneity still has to be addressed by cutting samples following a sampling route across the paddock and collecting a representative sample. That is again time intensive.
When making a purchase decision for an herbage measurement tool, other aspects should be considered besides the differences in the labor time requirement between tools; for example, the costs, the technology affinity, and the personal preferences of the user play a major role in the decision for or against a tool. While almost no skills are required for the use of the RPM, except the almost self-evident operating skills for using a smartphone, the UAV requires knowledge for the installation of the sensitive technology (training via tutorial videos), as well as the operation of a controller for flight control, the download of image data, and general handling on the desktop. Flying over the grassland with a UAV requires attention and patience, whereas walking across the paddocks with an RPM and sampling the paddocks by cutting fresh matter for NIRS analysis is more physically demanding.
In addition, possible supplementary benefits of a tool could play a role in the purchase decision. For example, the RPM measures distances on a farm with the integrated Global Positioning System module besides measuring herbage mass. During the RPM farm walk, users can check fences, inspect water troughs, and undertake small maintenance jobs. At the same time, or via the farm map generated by the UAV procedure, the quality of the pastures can be assessed, and decisions can be made regarding necessary mowing after grazing events, over-seeding, or fertilizer application. In contrast, the UAV and NIRS have the advantage of measuring parameters of grass quality, which is not yet possible with the RPM. This is especially important for farms with supplementary feeding, because they can use this information to control their concentrate allocation according to animal demands.
4.4. Opportunities of Precision Farming
Precision farming holds great potential to drive agro-ecological change and makes agricultural systems more ecologically and economically sustainable [55
]. The goal of the investigated smart farming tools is to increase the efficient use of grasslands, and subsequently to reduce the use of concentrates in dairy farming that may compete with human diets. Such a transformation of grazing systems contributes to Goal 12.2 of the Sustainable Development Goals, aiming to achieve sustainable management and efficient use of natural resources by 2030 [1
]. To pursue this goal and use grassland efficiently, it must be optimally managed. For this, knowledge on labor input becomes necessary. However, technical solutions are available to support labor and reduce the time requirement if implemented in optimized systems (workflows and farm conditions). In this way, a system can satisfy the Sustainable Development Goals 12 and 8. These are aimed at promoting occupational health and safety, as well as economic productivity and technological upgrading.
Promoting occupational health includes the reduction of the temporal workload of farm managers and employees, which our study shows can be achieved in two ways: firstly by using smart farming tools instead of the conventional approach, and secondly by further developing two smart farming tools (NIRS and UAV), where labor time can be saved through technological advancement and workflow optimization (Table 3
). The reduction in labor time, and thus labor costs, is associated with an economic advantage.
4.5. Limitations of the Study
By conducting herbage measurements, labor time is presumably reduced in other areas of feeding, farm management, and documentation. However, these aspects could not be quantified in our study because only the work operations for measuring herbage but not the farm as a whole production system were modeled in terms of the labor time requirement. Further time studies are needed to investigate the labor benefits of smart farming tools at the farm level.
The labor input for the investigated smart farming tools for near real-time herbage measurements on pastures is more favorable in contrast to the conventional approach of direct observations, except for the NIRS tool. However, the different work elements of the workflow are important to evaluate the labor input. For example, the number of sampled spots used for NIRS is an important factor. Under the conditions studied, the time for cutting two samples per paddock occupies about 83% of the total labor time requirement for the NIRS work procedure. Thus, NIRS is not temporally competitive with the other tools if two cut samples or more must be taken to determine the herbage quantity and quality representatively of the area. Apart from that, the required spot sampling is less advantageous in terms of measurement accuracy compared with RPMs or UAVs, making the NIRS work procedure less attractive for farms with topographically and botanically heterogeneous paddocks. It might be different if the NIRS and RPM tools were used in combination and grab samples were taken during the paddock walk.
Another important factor influencing the labor input is the size of the milking platform, where all work procedures require more time with increasing numbers of hectares to be measured. However, UAVs require disproportionately more time than the other smart farming tools for larger milking platforms (>80 ha) and are therefore not suitable for them. This is because the battery life of the consumer quadcopter used is currently not sufficient.
Both smart farming tools, UAV and NIRS, have the potential to additionally reduce labor input through further development and commercialization. The smart farming tools could contribute to sociotechnological sustainability because they potentially improve the work–life balance by reducing working hours.