Higher yields and lower methane emissions with new rice cultivars

This is the author accepted manuscript. The final version is available from Wiley via the DOI in this record.Breeding high-yielding rice cultivars through increasing biomass is a key strategy to meet rising global food demands. Yet, increasing rice growth can stimulate methane (CH4 ) emissions, exacerbating global climate change, as rice cultivation is a major source of this powerful greenhouse gas. Here, we show in a series of experiments that high-yielding rice cultivars actually reduce CH4 emissions from typical paddy soils. Averaged across 33 rice cultivars, a biomass increase of 10% resulted in a 10.3% decrease in CH4 emissions in a soil with a high carbon (C) content. Compared to a low-yielding cultivar, a high-yielding cultivar significantly increased root porosity and the abundance of methane-consuming microorganisms, suggesting that the larger and more porous root systems of high-yielding cultivars facilitated CH4 oxidation by promoting O2 transport to soils. Our results were further supported by a meta-analysis, showing that high-yielding rice cultivars strongly decrease CH4 emissions from paddy soils with high organic C contents. Based on our results, increasing rice biomass by 10% could reduce annual CH4 emissions from Chinese rice agriculture by 7.1%. Our findings suggest that modern rice breeding strategies for high-yielding cultivars can substantially mitigate paddy CH4 emission in China and other rice growing regions.This work was supported by the National Key Research and Development Program China (2016YFD0300903, 2016YFD0300501, and 2015BAC02B02), Special Fund for Agro-scientific Research in the Public Interest (201503122), Central Public interest Scientific Institution Basal Research Fund of Institute of Crop Science, the Innovation Program of CAAS (Y2016PT12, Y2016XT01), and the China Scholarship Council

Alternate wetting and drying system for water management in rice

Alternate wetting and drying (AWD) is aimed at saving water and maintaining comparable grain yields in the rice farming. It is a system of water management which involves the drying and rewatering of rice fields periodically. Rewatering is done to about 5 cm depth after the water level has fallen to 15 cm soil depth. This practice is repeated during the whole crop growing period except the flowering stage where the water level is maintained at up to 5 cm water depth. In order to get the best out of the AWD, it is important to select the right soil type, maintain the optimum plant population, apply nitrogen timely, and maintain the correct duration of wetting and drying. Fields under AWD may be ponded with water for 2-3 weeks for the cultural control of weeds. A good coordination among stakeholders may assist in attaining the maximum benefits from AWD. AWD also reduces arsenic in the rice grains and methane emission from the rice fields. It improves growth of root and canopy structure. Correct implementation of AWD can impart intended outputs on sustainable basis to tackle water scarce condition without losing rice productivity. © Springer Nature Singapore Pte Ltd. 2019

Satellite-Based observations reveal effects of weather variation on rice phenology

Obtaining detailed data on the spatio-temporal variation in crop phenology is critical to increasing our understanding of agro-ecosystem function, such as their response to weather variation and climate change. It is challenging to collect such data over large areas through field observations. The use of satellite remote sensing data has made phenology data collection easier, although the quality and the utility of such data to understand agro-ecosystem function have not been widely studied. Here, we evaluated satellite data-based estimates of rice phenological stages in California, USA by comparing them with survey data and with predictions by a temperature-driven phenology model. We then used the satellite data-based estimates to quantify the crop phenological response to changes in weather. We used time-series of MODIS satellite data and PhenoRice, a rule-based rice phenology detection algorithm, to determine annual planting, heading and harvest dates of paddy rice in California between 2002 and 2017. At the state level, our satellite-based estimates of rice phenology were very similar to the official survey data, particularly for planting and harvest dates (RMSE = 3.8-4.0 days). Satellite based observations were also similar to predictions by the DD10 temperature-driven phenology model. We analyzed how the timing of these phenological stages varied with concurrent temperature and precipitation over this 16-year time period. We found that planting was earlier in warm springs (-1.4 days °C-1 for mean temperature between mid-April and mid-May) and later in wet years (5.3 days 100 mm-1 for total precipitation from March to April). Higher mean temperature during the pre-heading period of the growing season advanced heading by 2.9 days °C-1 and shortened duration from planting to heading by 1.9 days °C-1. The entire growing season was reduced by 3.2 days °C-1 because of the increased temperature during the rice season. Our findings confirm that satellite data can be an effective way to estimate variations in rice phenology and can provide critical information that can be used to improve understanding of agricultural responses to weather variation

Weed Community Dynamics and System Productivity in Alternative Irrigation Systems in California Rice

Over the last 10 yr, California has experienced a series of ever-worsening droughts. Rice, traditionally a flooded crop, has come under increasing scrutiny with respect to its water use, leading to proposals to evaluate alternative irrigation systems. For growers, weed competition is one of the most limiting factors to maintaining high yields, so understanding the shifts among species in weed communities under the proposed alternative irrigation systems is vital. A field study was conducted from 2012 to 2014 to compare weed population and growth dynamics with three irrigation systems: (1) a conventional water-seeded control system (WS-Control), with a permanent flood of 10 to 15 cm from planting until 1 mo prior to harvest; (2) a water-seeded alternate wet and dry system (WS-AWD), with the field flooded from planting until canopy closure, after which floodwater was allowed to subside and the field was reflooded when the soil volumetric water content reached 35%; and (3) a drill-seeded alternate wet and dry system (DS-AWD), with rice drill seeded and then flush irrigated to establish the crop, after which the field was flooded until canopy closure and then underwent an alternate wet and dry (AWD) treatment similar to WS-AWD. In the AWD treatments, there were two drying periods, neither of which occurred after the heading stage. The dynamics of major weed species were evaluated using plant density counts (2012) and relative cover and biomass (2013 and 2014). Grasses (sprangletop and watergrass species) dominated the DS-AWD system; sedges, broadleaves, and grasses dominated both WS systems. The WS-AWD system increased smallflower umbrella sedge relative cover at canopy closure, relative dry weight at harvest, and percent frequency when compared with the WS-Control system. Yields did not differ across treatments when weeds were controlled (P > 0.05); in the absence of herbicides, yields in the WS-AWD were equivalent to the WS-Control (ranging from 40 to 65% of the herbicide-treated yields) and zero in the DS-AWD due to weed pressure. Nomenclature: bearded sprangletop, Leptochloa fusca (L.) Kunth N. Snow; ducksalad, Heteranthera rotundifolia (Kunth) Griseb.; redstem, Ammannia coccinea Rottb.; ricefield bulrush, Schoenoplectus mucronatus (L.) Palla; smallflower umbrella sedge, Cyperus difformis L.; rice (Oryza sativa L.)

Quantifying N leaching losses as a function of N balance: A path to sustainable food supply chains

Growing public concern over agricultural nitrogen (N) pollution is now reflected in consumers’ food choices and shareholders’ resolutions, causing rapid changes in global food supply chains. Nitrate (NO3) leaching represents the primary N source for groundwater contamination and freshwater ecosystem degradation. However, simplified science-based indicators are still lacking to facilitate improved N management practices at the farm-level. We conducted a global analysis of published field studies to evaluate N balance (N inputs minus N outputs) as a robust predictor for NO3 losses. Using 82 studies (1110 observations) for rainfed cereal crops in temperate regions, we 1) quantified the response of NO3 losses to changes in N balance for major rainfed cereal crops while accounting for environmental and management variables; and 2) assessed the feasibility of improving water quality through lower N balance under different scenarios using the case study of maize (Zea mays L.) data from the US Corn Belt. Observations were grouped in studies from the US and non-US regions. Results show that NO3 losses increased exponentially as N balance increases for both the US and non-US regions, though they were 60% higher in the US at a given N balance. Scenario analysis revealed that reducing the N rate from the agronomic optimum to the lower point within the economic optimum N rate range decreased NO3 losses by 13% without impacting economic returns. The case study for maize showed that improvements in N use efficiency that increase grain yield at a given fertilizer rate can substantially reduce N balance and mitigate NO3 losses. This study provides an evidence-based foundation for food supply chain companies to mitigate global NO3 pollution, specifically by using the generalized relationships presented here to track progress in NO3 leaching mitigation. To further resolve uncertainties and improve region-specific estimates for NO3 losses, we propose a tiered monitoring and assessment framework similar to the IPCC (Intergovernmental Panel on Climate Change) methodology for N2O emissions, widely implemented in science and used for policy

Predicting nitrate leaching loss in temperate rainfed cereal crops: Relative importance of management and environmental drivers

Nitrate (NO3) leaching from agriculture represents the primary source of groundwater contamination and freshwater ecosystem degradation. At the field level, NO3 leaching is highly variable due to interactions among soil, weather and crop management factors, but the relative effects of these drivers have not been quantified on a global scale. Using a global database of 82 field studies in temperate rainfed cereal crops with 961 observations, our objectives were to (a) quantify the relative importance of environmental and management variables to identify key leverage points for NO3 mitigation and (b) determine associated changes in crop productivity and potential tradeoffs for high and low NO3 loss scenarios. Machine learning algorithms (XGboost) and feature importance analysis showed that the amount and intensity of rainfall explained the most variability in NO3 leaching (up to 24 kg N ha-1), followed by nitrogen (N) fertilizer rate and crop N removal. In contrast, other soil and management variables such as soil texture, crop type, tillage and N source, timing and placement had less importance. To reduce N losses from global agriculture under changing weather and climatic conditions, these results highlight the need for better targeting and increased adoption of science-based, locally adapted management practices for improving N use efficiency. Future policy discussions should support this transition through different instruments while also promoting more advanced weather prediction analytics, especially in areas susceptible to extreme climatic variation