Combining plant biostimulants and precision agriculture

Introduction

Precision agriculture (PA) can encompass precision management of both plant and animal production, that is, precision crop management and precision livestock farming. PA can be considered as a technology-driven agricultural revolution with the potential of markedly changing the way farming is practised. Several definitions have been proposed in recent decades, but one of the best known definitions describes it as an approach to managing the agricultural production process by 'doing the right thing at the right time at the right place' (Gebbers and Adamchuk, 2010). A more complete and recent definition is: 'a management strategy that gathers, processes and analyzes temporal, spatial and individual data and combines it with other information to support management decisions according to estimated variability for improved resource use efficiency, productivity, quality, profitability and sustainability of agricultural production' (ISPA, 2019). This definition effectively summarizes the philosophy and the main objectives of PA:

• to take into account the variability in time and space of factors that affect the agricultural production process, and

http://dx.doi.org/10.19103/AS.2020.0068.14 © Burleigh Dodds Science Publishing Limited. 2020. All rights reserved.

• to improve efficiency in use of management inputs in agricultural

production.

Improving efficiency means using fewer resources to achieve the same result or getting a better result with the same use of inputs (e.g. water, fertilizers, plant protection products etc.). There is a close link between the philosophy on which PA is based and the need to increase sustainability and reduce the use of inputs that is potentially harmful for the environment (Stafford, 2018), including the need to reduce the impact of agriculture on global warming (Soto et al.,

2019). To achieve this aim, PA makes use of the best that technology can offer in terms of capacity to monitor spatial and temporal variability of soils and crops, and to implement management strategies that can take into account this variability, that is, site-specific or variable-rate management. In addition to environmental benefits, another objective of site-specific management is increasing cost-effectiveness in the application of inputs, such as seeds, fertilizers, plant regulators and plant-protection products. It should be noted that the economics of PA should take account of externalities, related, for example, to the reduction of the pollution of aquifers which reduces potential harm and cost for other users of water resources.

Biostimulants include a wide range of products, from non-microbial natural substances such as humic acids (HA), protein hydrolysates (PH) and seaweed extracts (SWE), to microbial-based products, for example, plant growth-promoting rhizobacteria (PGPR) (Rouphael and Colla, 2018). These are examined in detail in other chapters of this book. Their use, as soil or seed inoculants or in foliar spraying of vegetable or field crops, is increasingly considered as a sustainable way to promote plant growth, reduce abiotic stresses and increase nutrient and water-use efficiency (Calvo et al., 2014).

Biostimulants typically act on specific plant physiological processes. Seaweeds, for example, contain cytokinins and auxins or other hormone-like substances which stimulate plant hormones (Bulgari et al., 2015), whereas microbial inoculants stimulate plant growth and nutrient uptake by interacting with processes in the rhizosphere (Calvo et al., 2014). These mechanisms are subjected to a high degree of variability in time and space across agricultural fields, because of the differences in soil properties and plant phenotype interactions with the surrounding environment. The efficacy of biostimulants can therefore be highly dependent on application timing and placement (Preininger et al., 2018). Additionally the cost of biostimulants is generally high when compared, for example, to standard mineral fertilizers (Ronga et al., 2019a), so it would make sense for farmers to employ efficient strategies for their optimal application. PA is therefore an opportunity to enhance the cost-effectiveness of applying biostimulants by using variable-rate technology in different management zones. Some of the technologies developed in precision crop farming forthe site-specific application of other inputs such as fertilizers, herbicides and pesticides, could therefore be used in the application of biostimulants.

However, research and operational examples of the use of biostimulants in precision agriculture are critically lacking. The wide variety of biostimulants and their differing modes of action make it more difficult, compared to other standard farm inputs, to identify the correct application doses, timing and placing of individual products. This chapter reviews the research on monitoring of soil and crop variables which allows the use of PA in the application of biostimulants.

Monitoring spatial variability in soil and plants

In order to develop precision crop-management strategies, it is necessary to be able to monitor soil and crop properties at the field scale. There have been impressive advances in the tools available to monitor and map the spatial and temporal variability of crops and their status or soils, such as those based on remote and proximal sensing (Jones and Vaughan, 2010; Viscarra Rossel et al., 2011). The former use airborne or space-borne sensors, whereas the latter are defined as techniques in which the sensors are placed within 2 m or so of the target, typically on a ground-based platform such as a tractor. The advantage of remote and proximal sensing, as compared to traditional destructive methods in which crops are sampled from the field and taken to a laboratory for further analysis, is that they provide diagnostic information in a more rapid and cost- effective way and in a spatial context. It has been shown in many studies that remote and proximal sensing tools can detect plant abiotic and biotic stresses (Mahlein et al., 2019; Zarco-Tejada et al., 2012).

Optical sensors measure reflectance in the visible (0.4-0.7 pm), near- infrared (NIR, 0.7-1.3 pm) and shortwave infrared (SWIR, 1.3-2.5 pm) part of the spectrum, whereas thermal sensors detect heat emission in the thermal infrared spectrum (TIR, 7-20 pm). For the monitoring of plant stress or of traits linked with photosynthesis, there is increasing interest in the use of hyperspectral sensing (Transon et al., 2018) and of fluorescence (Meroni et al., 2009). Hyperspectral sensing, in which hundreds of narrow spectral bands are used, as compared to typically 4-13 wide bands used in multispectral sensors, was until recently, only possible using non-imaging ground-based spectroradiometers, but it is now becoming available on unmanned aerial vehicles (UAVs) (Aasen et al., 2018) and on satellite platforms (Pignatti et al., 2015) with imaging spectroscopy systems. Sensing the fluorescence of chlorophyll, or of other pigments, has been shown to be extremely useful for monitoring the photosynthetic process and the occurrence of abiotic or biotic stresses (Mahlein et al., 2019; Zarco-Tejada et al., 2012).

Remote sensing for water-stress detection has been generally done using the Crop Water Stress Index (CWSI) which uses TIR data (Jackson et al., 1988).

The CWSI is calculated using the sensed difference in canopy and ambient air temperatures, and vapour pressure deficit derived from meteorological information. There are crop stresses that can cause elevated plant temperature and are correlated with both crop water status and yield reduction (Pinter et al., 2003).

Proximal soil sensing aims at mapping soil variability and is generally based on several approaches such as the use of electrical and electromagnetic sensors, optical and radiometric sensors, sensors based on mechanical interactions between sensors and soil, and electrochemical sensors that can quantify the activity of specific ions or molecules (Viscarra Rossel et al., 2011).

Sensors can acquire measurements at specific times, for exam pie, in orderto explore spatial variability in the field (e.g. measuring soil electrical conductivity before sowing) or be stationary in the field, such as, for example, with wireless sensors networks (WSN) (Di Gennaro et al., 2017) and therefore quantify the temporal variability of given crop/soil properties. An important feature of most sensing systems is that the relationship between the actual measurement (e.g., voltage) and the soil/plant property is not directly measured by the sensor (Schirrmann et al., 2013). For example, a farmer might be interested in sensing the soil to quantify the amount of organic matter, but the sensor used to scan the soil will only detect the ability of the soil to conduct an electrical charge. Therefore, to quantify the relationship between the soil property and the sensor's output, some kind of site-specific inference is needed to relate the measurement to a given property.

In the context of precision application of biostimulants, for example, for those products especially targeted at abiotic stress mitigation or nutrient uptake improvement, the capability to monitor spatially and accurately the occurrence of these stresses, or of nutritional deficiencies, is crucial. Given that diagnosis and detection might be non-specific, it will be often desirable to combine multiple sensors, using data fusion approaches (Nawar et al., 2017; Viscarra Rossel et al., 2011). For example, Tsoulias et al. (2019) used apparent soil electrical conductivity measurements (ECa) as an indicator for soil water availability, and hence water stress, in an apple orchard and a tractor-based LiDAR scanning system to estimate the leaf area. A significant correlation was found between ECa and leaf area per tree. Trees in lower ECa areas had reduced leaf area and enhanced water stress. This kind of information enables precision application of biostimulants targeting water stress mitigation.

Site-specific management based on uniform management zones

Traditionally, agronomic management uses a whole-field approach where each field is considered as a homogenous area and spatial variability in the field is not considered. While this method is common among farmers because it is easy to implement, it is not efficient in optimizing input application. If a fertilizer is applied uniformly, some may be wasted (e.g. because crops already have adequate nutrition) and some be unused. The unused nutrients in some areas of the field are lostthrough leaching, runoff and gaseous emissions, resulting in an economic loss for the farmer and an environmental cost for the society (Basso et al., 2016). Yield is the result of interactions between factors such as soil, weather, cultivar, agronomic management, pest and diseases. Soil, for example, is highly variable across a field. Factors such as topography, land use and soil-plant-atmosphere interactions will also impact crop growth and development (Nawar et al., 2017).

Site-specific management (SSM) aims to manage soil and crops while taking into account spatial variability within a cultivated field. There are two main ways to carry out site-specific management:

• 1 constantly varying inputs (e.g. fertilizers, pesticides etc.) for each point of the field, in response to specific needs detected and quantified by sensors, or translated into previously produced maps using different data sources, and
• 2 dividing the plot into homogeneous sub-areas within which the inputs are applied uniformly within a site-specific management zone (MZ).

The latter approach, which requires the preparation of maps of uniform zones into which the field is divided, is a kind of compromise between conventional management and the first site-specific approach cited above, but is often more feasible than the former.

MZs are defined as sub-regions that present similar combinations of yield-limiting factors (Nawar et al., 2017; Miao et al., 2018). In such zones, a given agronomic input is applied at a given rate to maximize its efficiency. MZs can be generated by overlaying data of different sources including yield maps, topography, soil sampling data, aerial photographs, apparent electrical conductivity (ECa) and canopy images assessing crop growth variability (Maestrini and Basso, 2018a,b; Miao et al., 2018). Basso et al. (2011) have demonstrated how the subdivision of the field into MZs, combined with long-term weather information, has helped in quantifying optimal trade-offs between economic and environmental fertilization objectives. Delgado et al. (2005) have shown how the subdivision of fields into MZs helped to increase yield and nitrogen-use efficiency as well as reduce leaching.

Nowadays, the availability of data from different sources, such as yield maps, soil and crop sensing, topography, weather, and new geostatistical methods allows a better delineation of MZs (Nawar etal., 2017). These developments have improved the performance of MZ, as compared to traditional MZ delineation methods that were based only on soil or crop properties. In addition, the adoption of new sensing technologies for characterizing spatial variability in the soil, for example, gamma ray (Priori et al., 2016), electromagnetic induction (EMI) and visible and near-infrared (vis-NIR) spectroscopy (Viscarra Rossel et al., 2011) has helped to improve measurement accuracy. Measurement of variability in crop growth with proximal or satellite sensors to delineate MZs based on spectral indices (Pascucci et al., 2018) has meant that a wide range of yield-limiting factors in soil and crops can be measured rapidly at finer resolutions.