Simulation of future global warming scenarios in rice paddies with an open-field warming facility

To simulate expected future global warming, hexagonal arrays of infrared heaters have previously been used to warm open-field canopies of upland crops such as wheat. Through the use of concrete-anchored posts, improved software, overhead wires, extensive grounding, and monitoring with a thermal camera, the technology was safely and reliably extended to paddy rice fields. The system maintained canopy temperature increases within 0.5°C of daytime and nighttime set-point differences of 1.3 and 2.7°C 67% of the time

Estimation of Dynamic Canopy Variables Using Hyperspectral Derived Vegetation Indices Under Varying N Rates at Diverse Phenological Stages of Rice

Non-destructive and rapid estimation of canopy variables is imperative for predicting crop growth and managing nitrogen (N) application. Hyperspectral remote sensing can be used for timely and accurate estimation of canopy physical and chemical properties; however, discrepancies associated with soil and water backgrounds complicate the estimation of crop N status using canopy spectral reflectance (CSR). This study established the quantitative relationships between dynamic canopy nitrogen (CN) status indicators, leaf dry weight (LDW), leaf N concentration (LNC), leaf N accumulation (LNA), and CSR-derived new hyperspectral vegetation indices (HVIs), and to access the plausibility of using these relationships to make in-season estimations of CN variables at the elongation (EL), booting (BT), and heading (HD) stages of rice crop growth. Two-year multi-N rate field experiments were conducted in 2015 and 2016 in Hubei Province, China, using the rice cultivar Japonica. The results showed that the sensitive spectral regions were negatively correlated with CN variables in the visible (400–720 nm and 560–710 nm) regions, and positively correlated (r > 0.50, r > 0.60) with red and NIR (720–900 nm) regions. These sensitive regions are used to formulate the new (SR777/759, SR768/750) HVIs to predict CN variables at the EL, BT, and HD stages. The newly developed stepwise multiple linear regression (SMLR) models could efficiently estimate the dynamic LDW at the BT stage and LNC and LNA at the HD stage. The SMLR models performed accurately and robustly when used with a validation data set. The projected results offer a suitable approach for rapid and accurate estimation of canopy N-indices for the precise management of N application during the rice growth period

Development of a Critical Nitrogen Dilution Curve Based on Leaf Area Duration in Wheat

Precise quantification of plant nitrogen (N) nutrition status is essential for crop N management. The concept of critical N concentration (Nc) has been widely used for assessment of plant N status. This study aimed to develop a new winter wheat Nc dilution curve based on leaf area duration (LAD). Four field experiments were performed on different cultivars with different N fertilization modes in the Yangtze River basin and Yellow River basin in China. Results showed that the increase in LAD with increasing cumulative thermal time took the shape of an “S” type curve; whereas shoot N concentration decreased with increasing LAD, according to a power function. Both LAD and shoot N concentration increased with increasing N application. The new LAD based Nc dilution curve was determined and described as Nc = 1.6774 LAD−0.37 when LAD > 0.13. However, when LAD ≤ 0.13, Nc was constant and can be calculated by the equation when LAD = 0.13. The validation of Nc dilution curve with dataset acquired from independent experiments confirmed that N nutrition index (NNI) predictions based on the newly established Nc dilution curve could precisely diagnose N deficiency at different plant growth stages. The integrated N nutrition index (NNIinte), which was obtained by the weighted mean of NNI, was used to estimate shoot N concentration, shoot dry matter, LAD, and yield using regression functions. The linear relationships between NNIinte and these growth variables were well correlated. These results provided enough evidence that the new LAD–based Nc dilution curve could effectively and precisely diagnoses N deficiency in winter wheat crops

Spatial difference of climate change effects on wheat protein concentration in China

Climate change effects on global food security are not only limited to its effects on the yield of cereals but also their nutritional quality. However, climate change effects on crop nutritional quality, particularly grain protein concentration (PC) on a large geographical scale have not yet been quantified in China. For this purpose, we assessed the effects of three key climatic factors (temperature, precipitation, and solar radiation) on wheat PC in ten wheat-growing areas of China using a series of statistical models on a county-level PC dataset. The results demonstrated that the average PC in China from 2006 to 2018 ranged from 12.01% to 14.50% across the ten areas, with an obvious spatial difference pattern showing an increase in PC from south to north and from west to east. The sensitivity analysis indicated that PC showed a positive response to variation in the increasing temperature, and the PC of wheat grown in the Huanghuai area was less affected than the PC of wheat grown in other areas. Conversely, solar radiation posed negative effects on the PC in the southwestern area, whereas precipitation had intricate effects on the PC in all areas. Besides, the highest explanation of climate variability during five growth periods contributed 26.0%–47.6% of the PC variability in the northeastern area, whereas the lowest explanation of climate variability during five growth periods only accounted for 2.5%–3.7% of PC variability in the Yangtze River area. Our study further demonstrated that the effects of climate change on wheat grain PC in China were spatially heterogeneous with higher effects on PC in spring wheat-growing areas as compared to winter wheat-growing areas. We suggested that the northern and the northeastern area in China could be developed as alternative areas to produce wheat with high grain PC in the face of climate warming

Analyzing uncertainty in critical nitrogen dilution curves

International audienceNitrogen critical curves are frequently used to diagnose the N status of crops and grasslands. They play an important role in plant modelling and are frequently used in fertilizer management tools. During the last 20 years, a number of studies have been conducted for comparing critical curves obtained in various conditions (e.g., different cultivars) and understanding the origin of their differences. However, uncertainty in the determination of these curves is generally poorly analyzed in these studies, which increase the risk of false conclusions, in particular on the existence of differences between species, cultivars and cropping systems. Here, we present a Bayesian statistical model for estimating parameters of critical nitrogen dilution curve from experimental data. Contrary to standard methods commonly used for fitting critical nitrogen dilution curves, the proposed approach allows one to fit these curves in only one step, i.e., directly from the original biomass and nitrogen content measurements. Specifically, this method does not require the classification of nitrogen-limited data against non-nitrogen-limited data and does not necessitate the preliminary identification of critical nitrogen concentrations. Another advantage of the proposed method is that it can be easily implemented using freely-available software. We illustrate its practical interest using experimental data collected for winter wheat in France, and for maize and rice in China. We show that this method is useful for analyzing uncertainty in the fitted critical nitrogen curves and for comparing several curves obtained for different crop species and cultivars. The proposed method is based on the specification of prior probability distributions defining plausible ranges of values for the critical curve parameters, and we show here that it is preferable to use prior distributions that are not very informative if we want to limit their influence on the final result

Exploring the Impacts of Genotype-Management-Environment Interactions on Wheat Productivity, Water Use Efficiency, and Nitrogen Use Efficiency under Rainfed Conditions

Wheat production under rainfed conditions is restrained by water scarcity, elevated temperatures, and lower nutrient uptake due to possible drought. The complex genotype, management, and environment (G × M × E) interactions can obstruct the selection of suitable high yielding wheat cultivars and nitrogen (N) management practices prerequisite to ensure food security and environmental sustainability in arid regions. The agronomic traits, water use efficiency (WUE), and N use efficiencies were evaluated under favorable and unfavorable weather conditions to explore the impacts of G × M × E on wheat growth and productivity. The multi-N rate (0, 70, 140, 210, and 280 kg N ha−1) field experiment was conducted under two weather conditions (favorable and unfavorable) using three wheat cultivars (AUR-809, CHK-50, and FSD-2008) in the Pothowar region of Pakistan. The experiments were laid out in randomized complete block design (RCBD), with split plot arrangements having cultivars in the main plot and N levels in the subplot. The results revealed a significant decrease in aboveground biomass, grain yield, crop N-uptake, WUE, and N use efficiency (NUE) by 15%, 22%, 21%, 18%, and 8%, respectively in the unfavorable growing season (2014–2015) as compared to favorable growing season (2013–2014) as a consequence of less rainfall and heat stress during the vegetative and reproductive growth phases, respectively. FSD-2008 showed a significantly higher aboveground biomass, grain yield, crop N-uptake, WUE, and NUE as compared to other wheat cultivars in both years. Besides, N140 appeared as the most suitable dose for wheat cultivars during the favorable growing season. However, any further increase in N application rates beyond N140 showed a non-significant effect on yield and yield components. Conversely, the wheat yield increased significantly up to 74% from N0 to N70 during the unfavorable growing season, and there was no substantial difference between N70–N280. The findings provide opportunities for maximizing yield while avoiding excessive N loss by selecting suitable cultivars and N application rates for rainfed areas of Pothowar Plateau by using meteorological forecasting, amount of summer rainfall, and initial soil moisture content

Exploring the Impacts of Genotype-Management-Environment Interactions on Wheat Productivity, Water Use Efficiency, and Nitrogen Use Efficiency under Rainfed Conditions

Wheat production under rainfed conditions is restrained by water scarcity, elevated temperatures, and lower nutrient uptake due to possible drought. The complex genotype, management, and environment (G × M × E) interactions can obstruct the selection of suitable high yielding wheat cultivars and nitrogen (N) management practices prerequisite to ensure food security and environmental sustainability in arid regions. The agronomic traits, water use efficiency (WUE), and N use efficiencies were evaluated under favorable and unfavorable weather conditions to explore the impacts of G × M × E on wheat growth and productivity. The multi-N rate (0, 70, 140, 210, and 280 kg N ha−1) field experiment was conducted under two weather conditions (favorable and unfavorable) using three wheat cultivars (AUR-809, CHK-50, and FSD-2008) in the Pothowar region of Pakistan. The experiments were laid out in randomized complete block design (RCBD), with split plot arrangements having cultivars in the main plot and N levels in the subplot. The results revealed a significant decrease in aboveground biomass, grain yield, crop N-uptake, WUE, and N use efficiency (NUE) by 15%, 22%, 21%, 18%, and 8%, respectively in the unfavorable growing season (2014–2015) as compared to favorable growing season (2013–2014) as a consequence of less rainfall and heat stress during the vegetative and reproductive growth phases, respectively. FSD-2008 showed a significantly higher aboveground biomass, grain yield, crop N-uptake, WUE, and NUE as compared to other wheat cultivars in both years. Besides, N140 appeared as the most suitable dose for wheat cultivars during the favorable growing season. However, any further increase in N application rates beyond N140 showed a non-significant effect on yield and yield components. Conversely, the wheat yield increased significantly up to 74% from N0 to N70 during the unfavorable growing season, and there was no substantial difference between N70–N280. The findings provide opportunities for maximizing yield while avoiding excessive N loss by selecting suitable cultivars and N application rates for rainfed areas of Pothowar Plateau by using meteorological forecasting, amount of summer rainfall, and initial soil moisture content

Determination of critical nitrogen dilution curve based on stem dry matter in rice.

Plant analysis is a very promising diagnostic tool for assessment of crop nitrogen (N) requirements in perspectives of cost effective and environment friendly agriculture. Diagnosing N nutritional status of rice crop through plant analysis will give insights into optimizing N requirements of future crops. The present study was aimed to develop a new methodology for determining the critical nitrogen (Nc) dilution curve based on stem dry matter (SDM) and to assess its suitability to estimate the level of N nutrition for rice (Oryza sativa L.) in east China. Three field experiments with varied N rates (0-360 kg N ha(-1)) using three Japonica rice hybrids, Lingxiangyou-18, Wuxiangjing-14 and Wuyunjing were conducted in Jiangsu province of east China. SDM and stem N concentration (SNC) were determined during vegetative stage for growth analysis. A Nc dilution curve based on SDM was described by the equation (Nc = 2.17W(-0.27) with W being SDM in t ha(-1)), when SDM ranged from 0.88 to 7.94 t ha(-1). However, for SDM < 0.88 t ha(-1), the constant critical value Nc = 1.76% SDM was applied. The curve was dually validated for N-limiting and non-N-limiting growth conditions. The N nutrition index (NNI) and accumulated N deficit (Nand) of stem ranged from 0.57 to 1.06 and 51.1 to -7.07 kg N ha(-1), respectively, during key growth stages under varied N rates in 2010 and 2011. The values of ΔN derived from either NNI or Nand could be used as references for N dressing management during rice growth. Our results demonstrated that the present curve well differentiated the conditions of limiting and non-limiting N nutrition in rice crop. The SDM based Nc dilution curve can be adopted as an alternate and novel approach for evaluating plant N status to support N fertilization decision during the vegetative growth of Japonica rice in east China

Assessment of CSM–CERES–Rice as a Decision Support Tool in the Identification of High-Yielding Drought-Tolerant Upland Rice Genotypes

Drought is considered as one of the critical abiotic stresses affecting the growth and productivity of upland rice. Advanced and rapid identification of drought-tolerant high-yielding genotypes in comparison to conventional rice breeding trials and assessments can play a decisive role in tackling climate-change-associated drought events. This study has endeavored to explore the potential of the CERES–Rice model as a decision support tool (DST) in the identification of drought-tolerant high-yielding upland rice genotypes. Two experiments mentioned as potential experiment (1) for model calibration under optimum conditions and an experiment for yield assessment (2) with three irrigation treatments, (i) a control (100% field capacity [FC]), (ii) moderate stress (70% FC), and (iii) severe stress (50 % FC), were conducted. The results from the yield assessment experiment indicated that the grain yield of the studied genotypes decreased by 24–62% under moderate stress and by 43–78% under severe stress as compared to the control. The values for the drought susceptibility index (DSI) ranged 0.54–1.38 for moderate stress and 0.68–1.23 for severe stress treatment. Based on the DSI and relative yield, genotypes Khao/Sai, Dawk Kham, Dawk Pa–yawm, Goo Meuang Luang, and Mai Tahk under moderate stress and Dawk Kha, Khao/Sai, Nual Hawm, Dawk Pa–yawm, and Bow Leb Nahag under severe stress were among the top five drought-tolerant genotypes as well as high-yielding genotypes. The model accurately simulated grain yield under different irrigation treatments with normalized root mean square error < 10%. An inverse relationship between simulated drought stress indices and grain yield was observed in the regression analysis. Simulated stress indices and water use efficiency (WUE) under different irrigation treatments revealed that the identified drought-tolerant high-yielding genotypes had lower values for stress indices and an increasing trend in their WUE indicating that the model was able to aid in decision support for identifying drought-tolerant genotypes. Simulating the drought stress indices could assist in predicting the response of a genotype under drought stress and the final yield at harvest. The results support the idea that the model could be used as a DST in the identification of drought-tolerant high-yielding genotypes in stressed as well as non-stressed conditions, thus assisting in the genotypic selection process in rice crop breeding programs