1. Introduction
Today, machinery and high technological devices used in agriculture are very heterogeneous throughout the world due to economic and environmental reasons. The wide range of environmental conditions, land use, and suitability differences of agricultural fields make possible a wide diversification of the technical ameliorations. Precision Agriculture (PA) is commonly defined as the process of doing the right action at the right place at the right time; therefore, PA is not just a technology, but rather a management philosophy that is made possible by new technologies [
1,
2]. Advancements in remote sensing, machinery control systems, crop modelling, weather monitoring, decision making, cloud computing, and big data analysis drive PA to the new revolution in agriculture named smart farming [
3]. These advancements enhanced the accuracy of PA applications and made them available for a broader range of farmers, allowing enhanced practices through the possibility to predict the occurrence of water or nutrient stresses and take real-time supported decisions. Collaboration between public and private sectors towards research, education, and innovation opportunities in precision agriculture is rising and under constant development [
2]. Fertilization is one of the most relevant targets of this new approach [
4]. Indeed, adjusting the N rate to the measured crop requirement increases crop N use efficiency [
5,
6] and reduces environmental risks [
7,
8,
9]. Delgado et al. 2005 [
10] reported that applying N using VR can reduce NO
3 leaching losses by 25%. The VR application is recognized to effectively reduce the carbon footprint and Greenhouse Gases (GHG) emissions as shown in Acutis et al. [
11].
The interaction between the N rate, soil, weather, and crop response is a complex system in which these factors vary spatially within the same field and temporally over the season [
12]. Managing this variability is the key aspect that distinguishes PA from conventional management [
13]. Understanding crop nutrition needs and supply balance should be the basis for the definition of optimum N fertilizer application. Different factors play a role in the optimum N rate, such as N supply from other sources, fertilizer costs, quality and quantity of the final product, and its price [
13,
14].
A review paper about proximal sensing crop monitoring [
15] analysed the feasibility of remote and proximal optical sensors to estimate N management-linked variables; it was pointed out that different factors can impact the perception of crop variability (e.g., sensor type, spatial resolution, standardization of sensor measurements), although they are strongly linked to location, year, and variety. Farmers frequently adopt proximal optical sensors rather than retrieving information from remote sensing due to the easier access to this technology [
16]. Proximal sensing can be classified in Unmanned Aerial Vehicles UAVs with different cameras mounted on them, or tractor-mounted sensors (TMS). The UAVs [
17,
18,
19,
20,
21] are massively used in agricultural systems [
17,
18,
19,
20,
21]. Proximal sensing equipment also used for VR fertilization is represented by Greenseeker [
22,
23,
24] and OptRx [
25].
The proximal sensing equipment is typically used to manage different field homogeneous zones, also known as management zones. They represent subfield regions with the same soil traits and hydrologic characteristics within which a single strategy (e.g., fertilization rate) is appropriate [
26,
27,
28]. Since it is now possible to map the maize yields and moisture level at harvest with very high spatial resolution, the major challenge is modulating the amount of fertilizer equally to match the crop demand [
29]. VR fertilization is a key aspect of fertilization prescription in precision agriculture, which typically involves multiple criteria and objectives. Practical motivation embraces the optimization of the trajectories in the field with a consequent reduction in the use of fuel and fertilizer, waste of pesticides, and labour hours [
26,
30]. In the present case study, located in eastern Lombardy (Italy), maize production is experiencing relevant variability, being caused mainly by the low price on the market and pest control regulations and limits, which results in increased imports from countries outside the EU [
31]. It was observed that dairy farmers hardly adhere to the organic recommended fertilizer application rates due to the high availability of manure and slurry [
28,
32,
33,
34,
35,
36]; however, to ensure high crop yields, topdressing mineral N is used despite the purchase and environmental costs [
37]. Even considering the current subsidized rates, mineral fertilizers still represent a substantial budget item in European farms [
38].
In an integrated crop and livestock farm system, which is characterized by slurry availability over the year, organic fertilizer should be used to enhance the production efficiency and farmer net return [
11], maximize grain yield mainly with the improvement of spatial homogeneity in the field, and improve the quality by increasing the grain protein content [
39]. A way to achieve these objectives is to implement precision farming management with the adoption of proximal sensors, as supported by the rural development plan (PSR) of the Lombardy Region, which has recently partly subsidized the equipment purchase by farmers [
40,
41]. The use of organic N fertilizers from recycled digestate waste makes the agricultural system more environmentally sustainable [
7,
11,
42,
43] and improves net farmer return [
44].
In this study, we compared the effect of topdressing FR derived from fertilization with VR nitrogen fertilization on maize yield in a 2-year field experiment in eastern Lombardy under the hypothesis that the N application at VR guided by an active optical TMS leads to (i) maintenance of the same productivity level, (ii) a reduction in the mineral N supply, (iii) improved intra-field spatial homogeneity of the maize grain yield. The topdressing N rate was estimated according to the fertilization plan calculated by the current legislation and availability of organic fertilizer and in VR by the crop vigour status measured with an on-the-go approach.
4. Discussion
This field experiment allowed us to test the effectiveness of the proximal sensor of advanced and available technology in reducing N fertilizer with no negative impact on maize grain yield. The regional and EU incentives make the technology accessible thanks to a discounted purchase because the correct use of the sensor aims at reducing the mineral N fertilization targeting limited N leaching and volatilization losses [
38,
39]. This experiment offered us the opportunity to operate under actual field conditions being characterized by high SOC and N contents due to the long-term application of on-farm available manure. Such conditions are frequent in the Po plain, where crop and livestock farms need to valorise the available manure to return N and organic matter to soils [
35,
46]. Under such a condition of high soil fertility, the VR fertilization may not express its potential of reducing the total N amount. On the contrary, this potential was observed in this study: an average of 15 kg N ha
−1 was saved annually under VR compared to FR. Moreover, VR resulted in comparable yield as no significant differences were detected between the two treatments (
p > 0.05). This outcome suggests that VR was able to balance the differences between heterogeneous areas (crop vegetation status) and results in a positive economic opportunity due to the concurrent fertilizer reduction and yield gain. The homogenous areas where a similar S-index was estimated with proximal sensor technology reflected the spatial variability of the soil properties and soil cover status [
46,
47]. This result agrees with [
26,
48,
49] in which comparable experiments were conducted on maize.
In our study, the S-index, Topdressing N, and yield in 2017 and 2018 showed different average values between the two years. This was observed throughout the region [
50] because of severe biotic stress due to European corn borer (
Ostrinia nubilalis) and fungal diseases causing a declining rate of crop production. In general, FR often results in a maize grain yield increase in response to increasing N rates if no water stress occurs [
26]. However, unlimited N doses are recognized to cause crop luxury consumption, which is a process to avoid in sustainable farming [
5,
49]. Generally, in the first year, the mean maize yield was consistent with that observed by the farmer in the previous years. Conversely, in 2018, both fixed and variable rate treatments resulted in lower yield than that observed in the previous year. In 2017, VR increased grain yield by approximately 4% compared to a uniform supply of the same N amount, even if such an increase was non-statistically significant. The high yield in the first year of the experiment was likely to cause a large amount of crop residue production [
51], which required more N to start C decomposition processes, and therefore a consistent part of the N distributed in the second year was sequestered by the microbial community and was not directly available to the maize. In 2018, the VR application increased grain yield by 3%. Rational N management associated with good agronomic practices would lead to better use of organic N and reductions in N losses resulting in preventing losses [
28] or the improvement of crop yield [
52]. The soil variability (i.e., SOC content) did not significantly interact with treatments (
Figure 4). This result confirms the hypothesis according to which livestock and crop farming are peculiar systems where the large availability of slurry applied at sowing masks any possible effect of SOC variability [
11,
36]. In this context, the N fertilization rate at topdressing can effectively reduce N losses and lead to economic and environmental sustainability.
The results obtained in the two years of the experiment encourage VR application even though the economic benefit is limited when the estimation is carried out at a field scale (~10 € ha
−1). However, the application of VR across the whole farm surface enhances the cost saving (~4500 € ha
−1). These findings highlight that this technology is appropriate only for large-scale adoption when no external economic incentives are provided by supporting programs. The net saving computed in this study is consistent with data reported in other studies regarding variable N rate application to maize [
13,
53]. At the field scale, Jin et al., (2019) [
54] reported that VR application in fields with high spatial heterogeneity and varying yields over time could be a potentially effective approach for increasing revenues.
In the present study, economic savings were determined without considering any additional costs. Although canopy sensing has been shown to be a potentially profitable technology, it is recognized that more comprehensive approaches that include weather, soil, and landscape information would improve the confidence of N recommendations.
5. Conclusions
The present study aimed at evaluating the effectiveness of the variable rate approach in reducing the N fertilization rate at topdressing while avoiding maize yield loss in intensive agricultural farming systems. The case study was a typical livestock and crop farm of the Po plain, where a consistent amount of organic N is produced and applied before sowing. In this context, the reduction of the N fertilization rate at topdressing is a goal for enhancing economic and environmental sustainability. The reduction was possible thanks to the application of the variable rate approach, which can be pursued with proximal optical sensor technology.
These results were aided by the Common Agricultural Practices (CAP) funding scheme called PSR (Rural development plan), which partially granted the purchase of the equipment (precision fertilizer spreader, automatic dGPS guidance, CropSpec vigour sensor).
This study outcome suggests that the variable rate treatment results in an overall reduction of N without causing a decrease in the maize grain yield. In addition, this treatment is responsible for reducing the yield variability within the field.
As a side effect of the direct economic benefits of reduced N fertilization, the expected reduction of N leaching and NO2 emissions enhance the sustainability of the studied intensive agricultural system.
The study also highlights the economic profitability of the variable rate treatment under the hypothesis to adopt it at a farm scale.