Modelling soil water conent in a tomato field: proximal gamma ray spectroscopy and soil-crop system models

Proximal soil sensors are taking hold in the understanding of soil hydrogeological processes involved in precision agriculture. In this context, permanently installed gamma ray spectroscopy stations represent one of the best space-time trade off methods at field scale. This study proved the feasibility and reliability of soil water content monitoring through a seven-month continuous acquisition of terrestrial gamma radiation in a tomato test field. By employing a 1 L sodium iodide detector placed at a height of 2.25 m, we investigated the gamma signal coming from an area having a ~25 m radius and from a depth of approximately 30 cm. Experimental values, inferred after a calibration measurement and corrected for the presence of biomass, were corroborated with gravimetric data acquired under different soil moisture conditions, giving an average absolute discrepancy of about 2%. A quantitative comparison was carried out with data simulated by AquaCrop, CRITeRIA, and IRRINET soil-crop system models. The different goodness of fit obtained in bare soil condition and during the vegetated period highlighted that CRITeRIA showed the best agreement with the experimental data over the entire data-taking period while, in presence of the tomato crop, IRRINET provided the best results.Comment: 18 pages, 9 Figures, 3 Table

Long-Term Monitoring of a Surface Flow Constructed Wetland Treating Agricultural Drainage Water in Northern Italy

Agricultural drainage water that has seeped into tile drainage systems can cause nitrogen and phosphorus pollution of the surface water bodies. Constructed wetlands (CWs) can help mitigate the effects of agricultural non-point sources of pollution and remove different pollutants from tile drainage water. In this study, hydrological and water quality data of a Northern Italian CW that has been treating agricultural drainage water since 2000 were considered to assess its ability to mitigate nitrogen and phosphorus pollution. The effects of such long-term operation on the nutrients and heavy metals that eventually accumulate in CW plants and sediments were also analysed. Since 2003, the CW has received different inflows with different nutrient loads due to several operation modes. However, on average, the outflow load has been 50% lower than the inflow one; thus, it can be said that the system has proved itself to be a viable option for tile drainage water treatment. It was found that the concentration of nitrogen and phosphorus in the plant tissues varied, whereas the nitrogen content of the soil increased more than 2.5 times. Heavy metals were found accumulated in the plant root systems and uniformly distributed throughout a 60 cm soil profile at levels suitable for private and public green areas, according to the Italian la

Agroalimentare Idrointelligente

Nell'ambito dell'agricoltura regionale risaltano le colture intensive di pieno campo in pianura quali pomodoro e mais. Si tratta di colture di pregio notoriamente assai sensibili allo stress idrico. Nell'impostazione concettuale del progetto si prende atto che per l'irrigazione ottimale attraverso tecnologie di precision farming e' necessario 1. mettere a sistema tutte le fasi del ciclo produttivo e 2. associare alla sperimentazione nuove tecnologie il controllo diretto sul campo. Questo approccio ha al cuore lo sviluppo sperimentale di una Piattaforma informatica di trattamento dei dati acquisiti e la loro integrazione nel sistema IRRINET, i.e. l'ufficialita' del "consiglio irriguo" in Emilia Romagna. Le fasi progettuali che determinano il flusso in ingresso nel fulcro del progetto prevedono lo sviluppo tecnologico di dimostratori destinati alla produzione industriale di: 1. una piattaforma tecnologica innovativa composta di unita' sensibili ai gas ed onde elettromagnetiche, 2. soluzioni integrate sensore-drone dedicate alle colture di mais e pomodoro. I dati di valutazione dello stress idrico forniti dalle attivita' di progetto, in ingresso in IRRINET, saranno restituiti dallo stesso come ricetta irrigua (rateo variabile). Affinché quest'ultima possa essere somministrata in modo efficiente e rendere credibile l'obiettivo dell'irrigazione ottimale, il progetto prevede lo sviluppo tecnologico di dimostratori destinati alla produzione industriale di: 1. un sistema di controllo per l'interfacciamento della ricetta irrigua con apparati di irrigazione semovente e 2. nuove combinazioni tecnologiche per il raggiungimento di una effettiva precisione degli apparati di irrigazione a rateo variabile. Il raggiungimento degli obiettivi di progetto introdurra' importanti innovazioni rispetto ai "consigli irrigui" oggi forniti a piu' grande scala da IRRINET (CER) o da Societa' private (spesso privi di validazione)

POSITIVE: A SMART IRRIGATION PROJECT FOR AGRICULTURE 4.0

POSITIVE (Scalable Operational Protocols for precision agriculture) is a precision agriculture project for variable rate irrigation designed to improve the functionality of the IRRIFRAME system, the irrigation advice service of the Emilia-Romagna region. POSITIVE is based on satellite remote sensing, the use of vegetation indices for crops, IoT (Internet of Things) technologies, Big Data and 4.0 irrigation machinery. A central server manages the information flows and provides variable rate irrigation maps for farmers as final users. The system is public and free. In the first year of "IRRIFRAME plus" system experimentation (improved version of IRRIFRAME through POSITIVE machinery), at the experimental farm located in Mezzolara di Budrio (BO - Italy), promising results have been obtained for maize, with a WUE (Water Use Efficiency) going from 4.2 g l-1 (with standard IRRIFRAME service) to 5.2 g l-1. For sparse crops, such as tomato and onion, results were not so satisfactory. The future years of experimentation will allow to improve the calibration of VI-crop coefficient (Kc) correlation in order to improve the response in condition of partial soil cover and wetting condition

Efficient dissipation of acetamiprid, metalaxyl, S-metolachlor and terbuthylazine in a full-scale free water surface constructed wetland in Bologna province, Italy: A kinetic modeling study

The study investigated the dissipation ability of a vegetated free water surface (FWS) constructed wetland (CW) in treating pesticides-contaminated agricultural runoff/drainage water in a rural area belonging to Bologna province (Italy). The experiment simulated a 0.1% pesticide agricultural water runoff/drainage event from a 12.5-ha farm by dissolving acetamiprid, metalaxyl, S-metolachlor, and terbuthylazine in 1000 L of water and pumping it into the CW. Water and sediment samples from the CW were collected for 4 months at different time intervals to determine pesticide concentrations by multiresidue extraction and chromatography-mass spectrometry analyses. In parallel, no active compounds were detected in the CW sediments during the experimental period. Pesticides dissipation in the wetland water compartment was modeled according to best data practices by fitting the data to Single First Order (SFO), First Order Multi-Compartment (FOMC) and Double First Order in Parallel (DFOP) kinetic models. SFO (except for metalaxyl), FOMC and DFOP kinetic models adequately predicted the dissipation for the four investigated molecules, with the DFOP kinetic model that better fitted the observed data. The modeled distribution of each pesticide between biomass and water in the CW highly correlated with environmental indexes as Kow and bioconcentration factor. Computed DT50 by DFOP model were 2.169, 8.019, 1.551 and 2.047 days for acetamiprid, metalaxyl, S-metolachlor, and terbuthylazine, respectively. Although the exact degradation mechanisms of each pesticide require further study, the FWS CW was found to be effective in treating pesticides-contaminated agricultural runoff/drainage water within an acceptable time. Therefore, this technology proved to be a valuable tool for mitigating pesticides runoff occurring after intense rain events

Performance of a full scale constructed wetland as ecological practice for agricultural drainage water treatment in Northern Italy

Non-point sources of pollution, primarily agricultural drainage waters, can cause eutrophication and deterioration of water bodies. Surface flow constructed wetlands (SFCWs) are an ecological solution that can represent an efficient barrier and prevent agricultural pollutants from reaching other ecosystems. However, to better manage them and to understand removal processes occurring, it is important to study SFCWs that are functioning for longer periods of time and assess their efficiencies. This study concentrates on a full-scale SFCW in the Northern Italy that is used for agricultural drainage water treatment since the year 2000. An in-deep monitoring done for two years (2018 and 2019) showed that the system achieved satisfactory retention of up to 82% for TSS and up to 78% for TN and NO3--N. TP retention seemed to be poor (-27%), but further analysis showed that the SFCW performed well in this aspect as well, and that it is important to include precipitation loads in the overall balance. Soil content of nutrients and different trace elements did not show considerable differences in respect to the beginning of the monitoring period, and the uptake rates of TN and TP by above-ground vegetation were in the range 19.0-26.3 and 1.6-2.1 g m-2, respectively

RTM Inversion through Predictive Equations for Multi-Crop LAI Retrieval Using Sentinel-2 Images

Near-real-time, high-spatial-resolution leaf area index (LAI) maps would enable producers to monitor crop health and growth status, improving agricultural practices such as fertiliser and water management. LAI retrieval methods are numerous and can be divided into statistical and physically based methods. While statistical methods are generally subject to high site-specificity but possess high ease of implementation and use, physically based methods are more transferable, albeit more complex to use in operational settings. In addition, statistical methods need a large amount of data for calibration and subsequent validation, and this is only seldom feasible. Techniques based on predictive equations (PEphysical) represent a viable alternative, allowing the partial combination of statistical and physical methods merits while minimising their shortcomings. In this paper, predictive equation-based techniques were compared with four other methods: two radiative transfer model (RTM) inversion methods, one based on neural network (NNET) and one based on a look-up table (LUT), and two empirical methods (one using empirical models based on vegetation indices and in situ data and one based on empirical models found in the scientific literature). The methods were chosen based on common use. To evaluate the performance of the studied methods, the coefficient of determination (R2), root mean square error (RMSE), and normalised root mean square error (nRMSE, %) between the estimates and in situ LAI measurements were reported. The best PEphysical results, achieved by the OSAVI index (RMSE = 0.84 m2 m−2), provided better performance for LAI recovery than the NNET-based RTM inversions (0.86 m2 m−2) or the estimates made by LUT (0.94 m2 m−2). Furthermore, the best PEphysical produced accuracies comparable to the best empirical model (RMSE = 0.71 m2 m−2), calibrated through in situ data, and similar to the best literature model (RMSE = 0.76 m2 m−2). These results indicated that PEphysical can be used to recover LAI with transferability comparable to literature models