According to Precision Agriculture (P.A.) principles, managing arable lands means considering the inherent variability of the cultivation and that generated by external factors in such a way that both farmers’ profitability and environmental stewardship turn out to be increased [1
]. Moreover, steady (or less variable) conditions, such as soil and boundary conditions, topography, hydrographic network, and their interactions with intrinsic variables, must be considered [4
]. Farming tasks, i.e., cultivation, seeding, fertilization, herbicide application, and harvesting, can be carried out by linking the mapped variables to appropriate farming decisions. Thanks to Global Navigation Satellite System technology and sensors’ development, such actions can currently be carried out with improved accuracy, reduced energy needs, and better timeliness. In particular, sensors can be used both in-situ and on-the-go recording modes, which enables the site-specific knowledge of soil status (e.g., apparent electrical conductivity sensors, gamma-radiometric soil sensors, and soil moisture devices), weather information, and physiological status of crops (e.g., nitrogen sensors). At the same time, the acquisition of farm-level imagery allows the transformation of the collected geo-referenced data into maps that are helpful in setting up management plans aimed at increasing farming efficiency throughout the years. When concerning crop production, prescription mapping is essential: if, on the one hand, any plant needs water, nutrients, and carbon dioxide for its growth, on the other, soil heterogeneity and topography unavoidably affect the production dynamic [6
Transitioning from conventional to precision agriculture practices requires farmers to adopt specific technology [7
]. However, farmers are conservative, so increasing their trust in these technologies is essential in facilitating the new technology breakthrough [8
]. Trust is built when users have a clear conception of the appliance’s functionality, even though they do not know all of the details [10
]. Introducing guidelines aimed at promoting new agro technical patterns that are based on P.A. principles could help to strengthen the confidence of farmers: the parallel set up of applicative examples and references (the so-called Living Labs) could also foster the adoption of such guidelines [11
Following the national public consultation on precision farming that the Italian Ministry of Agriculture [12
] set up to increase overall farming sustainability, at the CREA research facility of Treviglio (Italy), precision farming trials have been carried out that introduced soil mapping, followed by harvest monitoring, as the first steps. In this work we focused on the steps required for the assessment of prescription maps to improve crop production in light of the in-field variability with a twofold objective: (i) setting up an appropriate management plan that enables overcoming the site-specific limiting factors and improving crop yield uniformity, (ii) providing farmers with valuable hints to help them in the transition from conventional to precision agriculture to go beyond state of the art.
3. Results and Discussion
Soil apparent electrical resistivity (Figure 3
a) is an important indicator that relates directly and indirectly to soil properties [22
a shows the resistivity map of the first layer of soil investigated.
The apparent resistivity values presented in this study were highly variable and in line with the findings of Hunt [27
], who indicated that the electrical resistivity varies from minimal values (i.e., 1.5 Ω m and below) for wet clay soils to more than 2400 Ω m for massive and hard bedrocks. In this study, they ranged between 29 and 756 Ω m and resulted in being significantly related (p
-value < 0.05) to soil properties, such as soil texture, rock fragment content, total carbonates, and available phosphorus (Table 1
and Table 2
). When compared to the less resistive ones, the areas with high resistivity values showed higher rock fragment and total carbonates contents, coarser soil texture, and lower phosphorus content.
The geostatistical analysis tested various clustering solutions to give rise to the zoning of the farm fields. A good correlation exists between soil electrical resistivity and crop production characteristics (amounts and dry matter) when comparing the resulting maps (Figure 3
b). Table 2
reports the statistical indices evaluated, which resulted in the clustering solution that is displayed in Figure 3
Various potential clustering options were tested during the geostatistical analysis, which evaluated the grouping into two, three, and four clusters to determine the most appropriate solution. Such a comparison pointed out that two was the optimum number of classes, because all of the considered indexes, i.e., the Xie–Beni [28
], the Fukuyama-Sugeno [29
], the Partition coefficient [30
], and the Proportion Exponent [31
], had the lowest value for that grouping (Table 3
). The differences between the values of the indices obtained are comparable with those that were also obtained by Córdoba and Galarza [20
]. According to this, the classification of the management zones means that the final output exhibits large zones with coherent boundaries that result in more straightforward management of the information than in the case of classifications having many small and irregular zones.
Therefore, the two-zone clustering has the advantage of obtaining more homogeneous management zones. In greater detail, the output of the analysis shows that, no matter the grown crop, soil characteristics and variability identify zones where the yield is significantly far from optimal (the black zones in Figure 3
c), and this means that the decisions to be taken will necessarily involve the specific knowledge of the variables impairing the yield in such zones.
Furthermore, although the yield maps show that crop moisture was higher along the plot borders, which indicated a possible effect of the bordering uncultivated woody and shady areas, the analysis results showed that the spot presence of stony zones in the considered plots has a more remarkable effect on crop production.
The large percentage of stones strongly influenced the soil behavior in the black-colored zones. In addition, the soils of such zones are more sandy, more lacking in organic matter and available phosphorus, and they have more carbonates than those in the better areas: in short, they are less fertile. According to this, the utilization of specific manuring has been planned that employs a variable rate technology spreader that adapts and optimizes the amount of manure to be spread (and, afterwards, incorporated into the soil) according to the prescription map that result from this study with a subsequent real improvement of the soil environment, which makes it more suitable for plant growth.
According to some authors, soil-based maps cannot reproduce the actual yield levels, while crop monitoring can give rise to high accuracy, reliability, and discrimination ability management zone mapping [33
]. The zoning approach suffers from the hypothesis that soil characteristics would dominate the effect on crop performance, resulting in relatively similar crop yield patterns each year. This assumption only works when the yield-limiting parameters are permanent [34
]. Therefore, identifying subfield regions with homogeneous yield-limiting factors (homogeneous management zones) requires a deep understanding of field spatial variability and the interaction between the whole system of soil, crop, weather, and management practices (e.g., fertilizer, irrigation).
The method for preparing the prescription map used in this study considered the information from the resistivity that was observed in the three depths and from the previous year’s production map (Figure 3
b). It should be noted that the areas of different management that resulted from the clustering could change their shape if the sources of information change and, therefore, can undergo continuous adaptation when, time after time, new information on the fields is available. Such a consideration is in line with the findings of Rodrigues et al. [36
], who pointed out that the optimum number of management zones can change over time while applying and following the spatial-temporal variability of soil and corn yield,.
Furthermore, the methodology that was developed in this study envisaged following the protocol outlined by Córdoba [20
] to determine the number of most appropriate clusters from a statistical point of view, i.e., according to the statistically significant difference between the points belonging to different clusters. On the one hand, the development of the analytical outline in R software (open source) makes it available for implementation and application, but, on the other, it requires specific statistical and coding expertise.
The comparison with reality sees clustering as very critical, because the determination of the clusters should also consider the technical means that are capable of supporting this choice. In the present case, the two clusters option, which is the optimal combination from a statistical point of view, is also optimal from a practical perspective. The P.A. machinery ordinarily agricultural contractors use, albeit being technologically upgraded, may encounter difficulties in following more complex (highly clustered) prescription maps, because neither the means of production nor the technical characteristics of the equipment available in the farm could support them. Therefore, it is appropriate to study methods that consider the detail that the statistical procedures can achieve and the precision level of the machinery to find a proper in-farm application combination. The resulting zoning (Figure 3
c) resulted in being the same for Triticale and maize, which meant that the soil characteristics pointed outlined by A.R.P. do influence crop behavior more than the variability that is related to the crop itself.
Some studies recently examined the transfer schemes to incentivize the transition of farmers towards P.A. and enhance the ability of farmers to connect with the know-how, the networks, and the institutions to improve productivity [37
]: our results (that are in line with their findings) allow farmers to overcome the initial standstill, and put transitioning towards P.A. into practice to gain experience and, in turn, complement the information from numerical and computational analysis [39
The A.R.P. turned out to be a suitable surrogate for detailed and georeferred soil coring: cluster analysis of AR.P. and yield data provided an objective method to identify management zones for targeted sampling activities (e.g., soil nutrients, organic matter, pH, soil remediation, etc.), which enabled setting up and variable rate manure applications.
According to the results, improvements in product yields can be planned and achieved acting on variable rate distribution of fertilizers (manure in particular), while the bordering with uncultivated zones is of secondary importance.
The applied farm management information system allows for the management of high-value crops, efficiently increasing harvest quality and quantity, and helping farmers to make decisions. When transitioning to P.A., the optimized machinery enabling better matching of soil characteristics with crop requirements plays a key role: however, the importance of the acquirable data is not limited to the in-field variability, and it can also help farmers to manage factors from outside their plots of land.
The available information from the processing procedures always requires considering the technical means that are available at the farm level to find the most appropriate practical combination. Finally, the awareness of the optimized inputs is derived from the high quantity of information (mapping and sensing techniques) that could effectively integrate the farmers’ daily experience, which results in increasing perception and reception of precision farming.