11 Pags.- 7 Tabls.- 4 Figs.The increasing pig population in northeast Spain and the need to adequately manage the pig slurry (PS) generated demand the inclusion of PS in crop fertilisation plans. The aim of the present study was to maximise the amount of PS that can be applied to the crops without adverse environmental effects. Thus, a 3-year experiment was established to evaluate the response of flooded rice to different fertilisation strategies. The fertilisation strategies tested were two rates of PS applied before sowing: 170 kg NH4-N ha−1 (PS170 strategy) that would cover the entire rice N requirements and 120 kg NH4-N ha−1 (PS120 strategy) that would theoretically require N as top-dressing to reach maximum yields. These strategies were compared to the rate of mineral fertiliser applied before sowing at 120 kg NH4-N ha−1. Plant density; the presence of weeds, pests and diseases; head rice yield; and rice quality were not affected by the N source (PS or mineral). Maximum rice yields (5567–8235 kg ha−1) varied between years and were attained with the two PS fertilisation strategies. Maximum yields with the PS170 strategy were reached without top-dressing application in the three years, but nitrogen (N) as top-dressing was necessary to reach maximum yields with the PS120 strategy. The nitrogen fertiliser replacement value (NFRV) of the two PS strategies, 87% (PS170) and 96% (PS120) of ammonium N applied, were not significantly different. The high PS NFRV suggests that PS is an excellent N source and rates should be established considering PS ammonium N content. Agronomic and recovery N use efficiencies and unaccounted N were not significantly different between mineral and PS strategies when the same ammonium N rate was applied; however, when the total N was considered, N use efficiencies decreased, indicating that organic N is not taken up by the crop during the crop season.This study was funded by the National Institute for Agricultural and Food Scientific Research and Technology of Spain (INIA) and FEDER funds (RTA2010-0126-C02-01 and RTA2013-0057-C05-04). B. Moreno-García was granted a FPI-INIA fellowship.Peer reviewe

Irrigated rice requires intense water management under typical agronomic practices. Cost effective tools to improve the efficiency and assessment of water use is a key need for industry and resource managers to scale ecosystem services. In this research we advance model‐based decomposition and machine learning to map inundated rice using time‐series polarimetric, L‐band Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) observations. Simultaneous ground truth observations recorded water depth inundation during the 2019 crop season using instrumented fields across the study site in Arkansas, USA. A three‐component model‐based decomposition generated metrics representing surface‐, double bounce‐, and volume‐scattering along with a shape factor, randomness factor, and the Radar Vegetation Index (RVI). These physically meaningful metrics characterized crop inundation status independent of growth stage including under dense canopy cover. Machine learning (ML) comparisons employed Random Forest (RF) using the UAVSAR derived parameters to identify cropland inundation status across the region. Outcomes show that RVI, proportion of the double‐bounce within total scattering, and the relative comparison between the double‐bounce and the volume scattering have moderate to strong mechanistic ability to identify rice inundation status with Overall Accuracy (OA) achieving 75%. The use of relative ratios further helped mitigate the impacts of far range incidence angles. The RF approach, which requires training data, achieved a higher OA and Kappa of 88% and 71%, respectively, when leveraging multiple SAR parameters. Thus, the combination of physical characterization and ML provides a powerful approach to retrieving cropland inundation under the canopy. The growth of polarimetric L‐band availability should enhance cropland inundation metrics beyond open water that are required for tracking water quantity at field scale over large areas.

Soil fertilisation affects greenhouse gas emissions. The objective of this study was to compare the effect of different fertilisation strategies on N2O, CH4 emissions and on ecosystem respiration (CO2 emissions), during different periods of rice cultivation (rice crop, postharvest period, and seedling) under Mediterranean climate. Emissions were quantified weekly by the photoacoustic technique at two sites. At Site 1 (2011 and 2012), background treatments were 2 doses of chicken manure (CM): 90 and 170kgNH4(+)-Nha(-1) (CM-90, CM-170), urea (U, 150kgNha(-1)) and no-N (control). Fifty kilogram N ha(-1) ammonium sulphate (AS) were topdress applied to all of them. At Site 2 (2012), background treatments were 2 doses of pig slurry (PS): 91 and 152kgNH4(+)-Nha(-1) (PS-91, PS-152) and ammonium sulphate (AS) at 120kgNH4(+)-Nha(-1) and no-N (control). Sixty kilogram NH4(+)-Nha(-1) as AS were topdress applied to AS and PS-91. During seedling, global warming potential (GWP) was ~3.5-17% of that of the whole rice crop for the CM treatments. The postharvest period was a net sink for CH4, and CO2 emissions only increased for the CM-170 treatment (up to 2MgCO2ha(-1)). The GWP of the entire rice crop reached 17Mg CO2-eqha(-1) for U, and was 14 for CM-170, and 37 for CM-90. The application of PS at agronomic doses (~170kgNha(-1)) allowed high yields (~7.4Mgha(-1)), the control of GWP (~6.5MgCO2-eqha(-1)), and a 13% reduction in greenhouse gas intensity (GHGI) to 0.89kgCO2-eqkg(-1) when compared to AS (1.02kgCO2-eqkg(-1)).

With population growth and resource depletion, maximizing the efficiency of soybean (Glycine max [L.] Merr.) and rice (Oryza sativa L.) cropping systems is urgently needed. The goal of this study was to shed light on precise irrigation amounts and optimal agronomic practices via simulating rice–rice and soybean–rice crop rotations in the Agricultural Policy/Environmental eXtender (APEX) model. The APEX model was calibrated using observations from five fields under soybean–rice rotation in Arkansas from 2017 to 2019 and remote sensing leaf area index (LAI) values to assess modeled vegetation growth. Different irrigation practices were assessed, including conventional flooding (CVF), known as cascade, multiple inlet rice irrigation with polypipe (MIRI), and furrow irrigation (FIR). The amount of water used differed between fields, following each field’s measured or estimated input. Moreover, fields were managed with either continuous flooding (CF) or alternate wetting and drying (AWD) irrigation. Two 20-year scenarios were simulated to test yield changes: (1) between rice–rice and soybean–rice rotation and (2) under reduced irrigation amounts. After calibration with crop yield and LAI, the modeled LAI correlated to the observations with R² values greater than 0.66, and the percent bias (PBIAS) values were within 32%. The PBIAS and percent difference for modeled versus observed yield were within 2.5% for rice and 15% for soybean. Contrary to expectation, the rice–rice and soybean–rice rotation yields were not statistically significant. The results of the reduced irrigation scenario differed by field, but reducing irrigation beyond 20% from the original amount input by the farmers significantly reduced yields in all fields, except for one field that was over-irrigated.