Conference Agenda

Effective irrigation management is particularly challenging in rice cultivation, especially when the Alternate Wetting and Drying (AWD) strategy is employed. This technique requires frequent monitoring of water level in field-installed water tubes (WT) to determine the optimal timing for reflooding the field. Daily manual measurements are time-consuming and may limit large-scale implementation.

This study presents Smart-WT, a remote monitoring system for continuous measurement of ponding water level in rice paddies. The system is based on a fully open-source architecture and uses an Arduino Nano combined with a GSM board for data acquisition and transmission. The device is waterproof, low-power, and battery-operated (approximately three months with two-hour transmission intervals), with the option of photovoltaic integration. Water level is measured through an ultrasonic measuring sensor installed above a WT inserted in the soil, perforated below the soil surface and blind above, enabling measurements both above and below the soil surface.

Laboratory and field tests assessed sensor accuracy and the influence of pipe characteristics and environmental temperature, highlighting the importance of appropriate pipe sizing for reliable measurements.

During the 2023–2025 seasons, Smart-WT was used successfully on several rice farms to support AWD irrigation management under different field conditions

The management of nitrogen-based fertilizers is a key agronomic challenge, particularly in Mediterranean paddy environments such as the Lomellina area (Northern Italy), within the main rice-producing district in Italy. Despite the importance of the topic, few studies have investigated nitrate sources and transformations in Mediterranean paddy environments. This study systematically applies dual δ¹⁵N-δ¹⁸O isotope analysis of nitrate in the Lomellina phreatic aquifer to trace the origin and fate of nitrogen-bearing compounds within this rice agroecosystem. Thirteen monitoring campaigns were carried out at 25 monitoring points tapping the phreatic aquifer. Isotopic data were integrated with nitrate concentration measurements, land use information, and records of fertilizers applied at field scale. Results show that the highest nitrate concentrations (up to 164.5 mg/L) are mainly associated with NH₄⁺-based inorganic fertilizers, whereas concentrations between 5 and 15 mg/L can be attributed to a combination of fertilizer inputs, soil organic nitrogen and atmospheric deposition. The study’s findings provide a scientific basis for improving groundwater protection strategies in the Lomellina district and in comparable Mediterranean paddy contexts. This study was carried out in the context of the PROMEDRICE project (https://promedrice.org/; PRIMA-Section2–2022) funded, for the Italian partners, by MUR (Italian Ministry of University and Research).

Ensuring the sustainability of rice cultivation requires farm practices that improve resource efficiency, reduce environmental impacts, and reinforce the provision of ecosystem services (ESS), including biodiversity conservation, landscape value, cultural heritage, and natural resource protection. The valuation and prioritization of ESS can help resolve conflicts among stakeholders, support compensation mechanisms, and promote sustainable ecosystem management, while ensuring that social values are properly reflected in policy development.

This study, conducted in the Lower Mondego Valley, Portugal, within the PROMEDRICE project, aims to assess ESS associated with on-farm Best Management Practices in paddy rice systems. Relevant ecosystem services were identified through the Gioia methodology (1991), applied to qualitative data collected from stakeholder interviews. This inductive approach enabled not only the identification of the most relevant ESS in the study area, but also the definition of key intervention pathways to enhance their provision within paddy rice systems.

The main ESS identified include habitat provision, biodiversity maintenance, water regulation, and biological control and pollination. By linking these priorities to feasible intervention pathways, the study provides an evidence-based framework to better align agricultural policies with farmers’ and stakeholders’ objectives, while reducing barriers to the adoption of more sustainable practices aimed at enhancing ecosystem service provision.

A study is underway in the Lower Mondego Valley (LMV) to safeguard wa-ter resources, focused on assessing Best Management Practices (BMP) – wet-sowing and continuous flooding, dry-seeding and delaying flooding, alter-nate wetting and drying, drainage water recirculation, and optimized fertilization plans.
Field monitoring methodology utilized various forms and automatic sensors, observing agronomic practices, measuring field water levels, and applying paddies water balance method.
Land productivity results range from 4.4 to 7.0 tons/ha, water productivity varied between 0.60 to 1.05 kg/m3, irrigation water use between 1200 and 1400 m3/ha, with relative deep percolation and surface drainage around 33% and 30%. Regarding the effect of rice paddies on drainage water quality, there is generally a reduction in nitrates and salinity, indicating a positive impact of the crop on surface water quality. Regarding groundwater, the process is more complex, and the effects depend heavily on the local context of each farm and its interaction with upstream conditions.
This study provided a rational understanding of BMPs effects of rice cultiva-tion on water resource quality, with a significant adaptation of cultivation practices to field conditions in each farm location, concluding that the good practices minimize negative effects and ensure good soil and water conservation.

Enhancing the sustainability of rice cultivation is a significant challenge in Lomellina's monoculture systems in northern Italy. This study aimed to evaluate the impact of various agronomic practices on crop productivity and the quality of the final grain, as well as the environmental fate of pesticides.

In a two-year field trial, four treatments were compared in separate experimental paddy fields: liming, compost application, green manure incorporation (Vicia villosa and Lolium multiflorum), and an untreated control. To monitor pesticide dynamics, water samples of irrigation inflow, paddy water and soil pore water were collected at seven time points during cultivation. The four fields were moreover characterized from a hydrological point of view.

Green manure and compost produced the best yield results in 2025. Conversely, a temperature drop in September 2024 negatively affected these higher vigor treatments. The incorporation of green manure was associated with increased total arsenic concentration in white rice.

Regarding the dynamics of pesticides in soil and water, azoxystrobin emerged as the most critical compound in water, whereas glyphosate and its metabolite AMPA remained below the detection limit in all samples collected. Overall, no significant effect of the treatments on the behavior of the monitored pesticides was observed.

Widespread concerns about the environmental impact of plant protection products have highlighted the need for predictive exposure modelling and environmental risk assessment within the European regulatory framework. Estimating pesticide concentrations in paddy rice systems is challenging due to the complex hydrological dynamics of flooded areas. Among available tools, the numerical model RICEWQ is considered the most suitable to simulate pesticide fate in rice paddies. In this study, RICEWQ was coupled with RIVWQ to assess exposure in riverine systems and with VADOFT to evaluate vertical transport in soil. The environmental fate of six widely used herbicides in the Lomellina rice-growing area (Lombardy Region, northern Italy) was simulated under three rice irrigation strategies across four soil units. Predicted peak concentrations in receiving canal water remained below regulatory acceptable concentrations (RACs) and were consistent with recent monitoring data from ARPA-Lombardia (agency responsible for the environmental monitoring). Conversely, annual average groundwater concentrations at 1 m depth occasionally exceeded the drinking-water limit of 0.1 µg/L in vulnerable soils. Regression analyses identified infiltration rate and water management as key drivers influencing variability in groundwater concentrations. Numerical models provide reliable exposure estimates in rice-growing systems, although uncertainties in parameterization and model limitations constrain their applicability.

Under certain conditions, rice plots function as efficient nature-based filtration systems that improve water quality. In irrigated flood-irrigated fields, suspended sediments in the applied water settle within the plots, resulting in cleaner drainage discharge. Additionally, these systems can sequester nutrients and mitigate the presence of pesticides and herbicides.

This study evaluated the water depuration capacity of flood-irrigated rice fields in two Spanish irrigation districts: the Lower Guadalquivir Marshes and Baix Ter. Methodologies were tailored to each region’s specific characteristics. In the Lower Guadalquivir, we conducted paired measurements of suspended solids, nutrients and active ingredients, in both irrigation inflows and drainage outflows. In Baix Ter, we established a nitrogen balance across various environmental compartments to assess the impact of fertilization.

Results from the Lower Guadalquivir demonstrate a significant reduction in suspended solid concentrations in drainage water. In Baix Ter, findings indicate that standard nitrogen fertilization rates do not lead to increased nitrate levels in monitored groundwater. These findings highlight the potential for rice plots to serve as "green filters."

The broader applicability of these systems across other Mediterranean environments targeted by PRIMA funded Promedrice project is presented and discussed

Excessive fertilization in rice cultivation is one major environmental problem leading to soil and water pollution. Additionally, conventional fertilization requires multiple field entries during critical phenological stages, increasing production costs and labor requirements. Therefore, the development of rapid, reliable methods assessing crop nutritional status to adjust fertilizer rates ensuring environmental sustainability is critical.

Three irrigation methods dry-seeding with continuous flooding, alternate wetting and drying, and surface drip irrigation - combined with seven nitrogen treatments: a control, three conventional N rates (120, 180, and 240 kg N ha⁻¹), and three slow‑release N rates (120, 180, and 240 kg N ha⁻¹) were experimented.

Leaf chlorophyll content was assessed using a SPAD meter between the flowering and maturity stages and used as an indicator of plant nitrogen status in all treatments.

Additionaly, Multiple Linear Regression, Polynomial Linear Regression, Support Vector Regression, Extreme Gradient Boosting and Random Forest were applied to estimate nitrogen fertilization status in the differnt treatments from multispectral imagery acquired by UAV. Among the tested models, SVR provided the most accurate predictions, with strong agreement between measured and estimated SPAD values, demonstrating that integrating UAV‑based remote sensing with machine‑learning offers a reliable framework for assessing plant nitrogen status in rice cultivation.

Water scarcity and increasing soil salinity constrain rice production in the Guadalquivir River marshes (SW Spain), where seasonal flooding is essential for salt leaching and maintaining soil productivity. During the severe drought of 2023, rice cultivation was suspended and fields were either planted with winter crops (cereals and legumes) or left fallow. The absence of inundation provided a unique opportunity to evaluate how alternative winter management influences topsoil salinity dynamics and potential implications for subsequent rice performance.

A multi-year Sentinel-2 time-series analysis (2021–2025) was conducted across representative farms to assess temporal patterns in soil salinity and crop development. Vegetation indices (NDVI, GNDVI, GDVI) were used to characterise rice growth trajectories, while salinity-related indices (NDSI, ASTER_SI, SAVI) were calculated under bare soil conditions to ensure spectral consistency and minimise vegetation interference. Plot-level temporal trends, interannual variability, and deviations associated with the 2023 non-flooded season were quantified to detect management-related shifts.

Preliminary results indicate measurable interannual variability in surface salinity signals following the interruption of flooding, with contrasting responses between winter cropping and fallow systems. These findings highlight the relevance of satellite-based time-series monitoring as a diagnostic tool for salinity assessment and adaptive management in water-scarce rice agroecosystems.