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Effects of light intensity and temperatureThe germination of P. paradoxa (91 to 95%) and wheat (93 to 97%) was not affected by light intensity (data not shown). Our results conform to previous studies which revealed that light intensity had little role in influencing P. paradoxa germination24.The germination of wheat and P. paradoxa was influenced by temperature regimes (Fig. 1). At temperature regimes of 15/5 °C and 20/10 °C, germination of wheat and P. paradoxa did not vary. Seed germination in wheat remained similar at temperatures ranging between 15/5 °C to 30/20 °C. However, in P. paradoxa, germination was reduced at higher temperature regimes (35/25 C) compared with lower temperature regimes (15/5 °C to 25/15 °C). At the highest temperature regime (35/25 °C), the germination of wheat was 79%, while, at this temperature regime, the germination of P. paradoxa was only 1%. This suggests that wheat can germinate at high-temperature ranges, while, germination of P. paradoxa may be reduced at high temperatures (35/25 °C). These results implied that at the time of planting wheat in Australia if the air temperature is low, the chances of emergence of P. paradoxa are very high. This suggests that efforts should be made towards early control of P. paradoxa in wheat if the air temperature in the winter season falls early. These results also suggest that early planting of wheat could reduce the emergence of P. paradoxa as the prevailing temperature conditions are relatively high in early planting (e.g., end of April). In the Indo-Gangetic Plains, better control of P. minor was observed in the early planting of wheat (high-temperature conditions) due to less emergence of P. minor25.Figure 1Effect of alternating day/night temperatures (15/5 to 35/25 °C) on germination of Phalaris paradoxa and wheat seeds (incubated for 21 d) under light/dark (12-h photoperiod). LSD: Least significant difference at the 5% level of significance.Full size imagePrevious studies have also revealed that germination of P. paradoxa was highest at 10 °C and then failed to germinate at 30 °C 24,26, however, these studies were conducted at constant temperatures and the germination response of P. paradoxa was not studied in comparison with wheat in those studies.Effect of radiant heatThe germination of P. paradoxa seeds that were stored at room temperature (25 °C) was 97%, which reduced to 88% after exposure to the 100 °C pretreatment for 5 min and became nil at 150 °C (Fig. 2). About 88% of P. paradoxa at 100 °C suggests that it can tolerate heat stress for short periods.Figure 2Effect of high-temperature pretreatment for 5 min (℃) on germination of Phalaris paradoxa seeds. LSD: Least significant difference at the 5% level of significance.Full size imageGermination was nil at 150 °C and above, suggesting that burning could help in managing P. paradoxa, particularly in a no-till field where seeds are on the soil surface or at shallow depths. Exposure of seeds to fire could inhibit germination by desiccating the seed coat or by damaging the embryo27,28,29.Burning of residue in the fields could kill weed seeds and other pests in the topsoil layer30. Windrow burning proved to be an effective tool for killing weed seeds in paddocks31. However, the crop residue burning may cause environmental destruction by killing microbes and polluting the air. Also, it reduces the amount of soil organic matter due to the high heat, causing soil degradation. Therefore, these aspects should also be considered while formulating weed management strategies through crop residue burning. Burning may also release the dormancy of other weed seeds present in the subsoil and thus may increase infestation; therefore, this technique should be used cautiously32,33.Effect of osmotic stressGermination of P. paradoxa was highest (95%) in the control treatment and germination reduced to 75% at an osmotic potential of −0.8 MPa, and became nil at −1.6 MPa (Fig. 3). However, in wheat, germination did not reduce with an increase in water potential and it was 94% in the control treatment.Figure 3Effect of osmotic potential on germination of Phalaris paradoxa and wheat seeds at alternating day/night temperatures of 20/10 °C under 12 h photoperiod. Seeds were incubated for 21 d. LSD: Least significant difference at the 5% level of significance.Full size imageAt a very high concentration of PEG, the metabolic activity of P. paradoxa might be reduced due to water stress. Seed germination is affected when seeds are not able to get critical moisture threshold levels for imbibitions34,35. These results indicate that high water stress may inhibit the seed germination of P. paradoxa. However, under no water stress or mild water stress conditions, P. paradoxa may infest the wheat crop.Contrary to these results, previous studies reported that germination of P. paradoxa was reduced by 90% at an osmotic potential of −0.25 MPa25. Good germination of wheat at high osmotic potential indicates that the wheat variety used in this study may have water stress tolerance traits for germination. It was observed that wheat could germinate well (75%) at a high-water stress level (−1.6 MPa)36. This suggests that it is possible to menace P. paradoxa by growing stress-tolerant varieties of wheat and manipulating irrigation. In a previous study, less infestation of P. paradoxa was observed in drip-irrigated wheat crops due to optimal soil moisture conditions for the crop37.Effect of salt stressGermination of P. paradoxa was highest (93%) in the control treatment, and at a NaCl of 150 mM, germination was reduced to 76% (Fig. 4). Similarly, in wheat, germination was highest (94%) in the control treatment and at a salt concentration of 150 and 200 mM, germination was reduced to 84 and 79%, respectively. These results suggest that at a high salt concentration, P. paradoxa may infest the wheat crop owing to its ability to germinate under high salt concentrations.Figure 4Effect of sodium chloride concentration on germination of Phalaris paradoxa and wheat seeds at alternating day/night temperatures of 20/10 °C under 12 h photoperiod. Seeds were incubated for 21 d. LSD: Least significant difference at the 5% level of significance.Full size imageContrary to this, in Iran, it was observed that germination of P. paradoxa was reduced by 70% at a NaCl of 160 mM24. Most of the Australian soils are saline; therefore, it is quite possible that P. paradoxa in Australia might have developed traits for salt tolerance38. The variable response of populations of P. paradoxa to salt concentrations in Iran and Australia might be due to genetic differences between the P. paradoxa populations38. These observations suggest that P. paradoxa could invade the agroecosystem under the saline conditions of Australia.Effect of seed burial depth on emergenceGermination of P. paradoxa was very low (10%) on the soil surface, and seedling emergence was highest (74%) at a soil burial depth of 0.5 cm (Fig. 5). Seedling emergence was similar when seeds were buried in the soil at a depth ranging from 0.5 to 4 cm. Seedling emergence was 32% at a burial depth of 8 cm.Figure 5Effect of seed burial depth on seedling emergence of Phalaris paradoxa. LSD: Least significant difference at the 5% level of significance.Full size imageThe results from this experiment suggest that a no-till production system may inhibit the germination of P. paradoxa. This study also suggests that deep tillage ( > 4 cm) could reduce the emergence of P. paradoxa to some extent; therefore, inversion tillage could be a weed management strategy if the seedbank is in the shallow layer of the soil. It has been reported that the emergence of small-seeded weeds is reduced from deeper burial depths, as the soil-gas exchange is limited 21. However, it is important to know the seed longevity of this weed in different soil and environmental conditions when considering tillage operations39.Likewise, previous studies also reported that seed germination of P. paradoxa was lowest on the soil surface and no seedlings emerged from a soil depth of 10-cm2,40. Contrary to this in Iran, germination of P. paradoxa was found to be  > 65% on the soil surface 24.Evaluation of PRE-herbicidesResults revealed that cinmethylin, pyroxasulfone, and trifluralin provided 100% control of P. paradoxa. Atrazine, bixlozone, imazethapyr, isoxaflutole, prosulfocarb + s-metolachlor, and s-metolachlor were not found to be effective against P. paradoxa (Table 1). Pendimethalin and triallate controlled P. paradoxa by 80 and 42%, respectively, compared with the nontreated control.Table 1 Effect of PRE herbicides on the survival of Phalaris paradoxa and wheat seedlings (28 d after spray).Full size tableIn wheat, all tested herbicides performed similarly for plant survival except dimethenamid-P and prosulfocarb + s-metolachlor, which caused wheat mortality by 41 and 16%, respectively, compared with the nontreated control. These results suggest that pyroxasulfone, pendimethalin, and trifluralin can be successfully used for the management of P. paradoxa in wheat. Alternative use of these herbicides in wheat crops could provide sustainable weed control of P. paradoxa. In previous studies conducted in Australia, herbicides namely cinmethylin, pyroxasulfone, and trifluralin were found safe for wheat and provided excellent grass weed control41.Efficacy of PRE-herbicides in relation to crop residue coverCinmethylin, pendimethalin, and pyroxasulfone were proven to be very effective against P. paradoxa under no residue cover conditions (Table 2). However, at the residue cover of 6 t ha-1 (high output systems), the efficacy of these herbicides decreased and these three herbicides failed to provide effective control of P. paradoxa. At the residue cover of 2 t ha-1 (low output systems), the efficacy of pyroxasulfone in controlling P. paradoxa was not affected; however, cinmethylin and pendimethalin at the residue load of 2 t ha-1 did not control P. paradoxa. These results suggest that in a residue-retained, no-till system, pyroxasulfone could provide better control of P. paradoxa compared with cinmethylin and pendimethalin.Table 2 The interaction of PRE herbicides and wheat residue amount on the survival of Phalaris paradoxa seedlings at 28 d after spray.Full size tableThe crop residue binds some herbicides, which results in a reduced dose to target weeds and provides poor weed control42. A crop residue cover of 1 t ha-1 may prevent 50% of the herbicide from reaching the target weed seeds in the soil and thus provide poor weed control43.Efficacy of POST herbicides in relation to plant sizeWhen plants were sprayed at the 4-leaf stage, the herbicides clodinafop and propaquizafop were not effective against P. paradoxa compared with the other tested herbicides (Table 3). The efficacy of clethodim, glyphosate, haloxyfop, and paraquat in controlling P. paradoxa was not decreased even when plants were sprayed at the 10-leaf stage. In previous studies, poor control of P. paradoxa was observed with ACCase-inhibiting herbicides44,45. These results also suggest that under noncropped or fallow situations, early and late cohorts of P. paradoxa can be controlled successfully by delaying applications of clethodim, paraquat, haloxyfop, and glyphosate.Table 3 The interaction effect of plant size (large plants-10 leaves and small plants-4 leaves) and herbicide treatments on the survival of Phalaris paradoxa seedlings at 28 d after spray.Full size tableGermination of P. paradoxa at 25/15 °C (day/night) was lower compared with 20/10 °C. This suggests that early sowing of wheat (relatively high-temperature conditions) could reduce the emergence of P. paradoxa in fields. Phalaris paradoxa did not germinate after exposure to radiant heat of 150 °C (for 5 min), which suggests that burning may be a useful tool for managing P. paradoxa, particularly when seeds are on the soil surface or at the shallow surface. A high level of tolerance of P. paradoxa to water and salt stress was observed. These observations suggest that this weed can dominate under saline and water stress conditions in Australia. Low germination of P. paradoxa was observed on the soil surface, suggesting that a no-till system could provide better control of P. paradoxa. PRE herbicides cinmethylin, pyroxasulfone, pendimethalin, and trifluralin were effective for control of P. paradoxa in wheat; however, under a conservation tillage system, pyroxasulfone provided better control of P. paradoxa compared with other herbicides. Haloxyfop and clethodim were the most effective herbicides among the ACCase-inhibiting herbicides. Under noncropped or fallow land situations, larger plants of P. paradoxa can be successfully controlled with the application of clethodim, glyphosate, and paraquat. More

The giant panda’s preference for culm over leaves occurred even though leaves had far more protein than did culm, which is inconsistent with the suggestion that giant pandas are high protein carnivores1. The giant panda’s preference for culm over leaves in the spring was likely driven by the increased availability of mono- and polysaccharides in culm relative to leaves31. This preference by giant pandas for a high-carbohydrate, low protein diet is similar to the brown bear’s preference for carbohydrate-rich but protein-poor berries or apples over protein- and energy-rich salmon, although both needed to be consumed to produce the most efficient diet2,10. The preference for culm over leaves created a protein ME in the diet of giant pandas from January to March (~ 20%) when digestible carbohydrates were most plentiful and for the entire year (27 ± 10%) that was comparable to the macronutrient proportions in giant panda milk and the milk and diets selected by other ursids (Table 1, Fig. 3) that minimize energy expenditure and maximize the efficiency of gain3.Table 1 The protein and fat metabolizable energy concentrations (%) in ursid milks and in the diets selected by brown bears, polar bears, and sloth bears when given ad libitum access to foods rich in protein, fat, and digestible carbohydrates (PFC) or protein and fat only (PF)1,3,4,29,32,40,54,55.Full size tableRelative to the suggestion that giant pandas are not well adapted to consuming the more omnivorous macronutrient proportions characteristic of the diets of other ursids1, captive giant pandas are often fed various combinations of bamboo and high-carbohydrate supplements that include rice, baby cereal, bread, beans, wheat, millet, apples, carrots, ground corn, sorghum, sugar cane, and sugar in addition to milk, eggs, vegetables, and various meats5,32,33. The dry matter of giant panda diets in five Chinese zoos in which successful reproduction occurred (i.e., Beijing Zoo, Chengdu Zoo, China Conservation and Research Center, Fuzhou Zoo, and Xian Zoo) averaged 11.6 ± 2.4% protein, 39.0 ± 13.6% neutral detergent fiber (NDF) or cell wall, 5.0 ± 2.0% fat, and 5.4 ± 0.6% ash32. If we estimate soluble carbohydrates as 100 – (NDF + protein + fat + ash)3, the soluble carbohydrate content was 39.0 ± 11.2%. This approach likely underestimates digestible carbohydrates in that it assumes a zero digestibility for the hemicellulose fraction of the NDF. However, even with these assumptions, the average macronutrient ME distribution was 19 ± 4% protein, 18 ± 7% fat, and 63 ± 18% carbohydrate, or again a low-protein macronutrient ratio typical of the other ursid diets (Table 1).Several errors may have been made in the previous giant panda study1 that likely influenced their conclusion. These included initially air-drying their bamboo samples in a dark room prior to laboratory drying and analyses34. When plants are cut and allowed to dry slowly, soluble carbohydrates are lost as they are metabolized to carbon dioxide, water, and energy until death of the plant cells35,36. The loss of soluble carbohydrates increases when drying occurs slowly, as would occur with air-drying in a dark room. Protein also may be metabolized, but the nitrogen remains and is only converted to different nitrogen-containing compounds, such as amides, free amino acids and peptides that would be part of a crude protein estimate36.Thus, if there are significant amounts of soluble carbohydrates in fresh bamboo, air-drying of bamboo samples will lead to an underestimate of the importance of carbohydrates and thereby an overestimate of the importance of protein. Indeed, starch accounted for 16 ± 11% of the digestible macronutrients and 23 ± 13% of the digestible carbohydrates in bamboo during the current study. Also, the previous study1 assumed a hemicellulose digestibility of 22%37, which significantly underestimated that found in our digestion studies (46 ± 9%).Another potential error in the previous study1 was in using a concept they termed “relative efficiencies” of macronutrient absorption in which the macronutrient profiles of bamboo were directly compared to that of giant panda feces. Such a comparison is often meaningless without knowing the amounts of food consumed and feces produced because the proportions of macronutrients in the feces reflect the extraordinarily complex interaction between the variable absorption of digestible products, passage of indigestible components, and excretion of metabolic products. Thus, only by providing data showing a close linkage between relative efficiencies and digestibility or measuring digestibility as we did can one be certain of estimating the relative importance of macronutrients.The macronutrient intake of wild sloth bears has not been measured, although the dietary proportions and energy content of termites, ants, and fruits have been estimated17. Soldiers and worker termites and ants are generally low in fat and high in protein (excluding the nitrogen in their chitin exoskeleton), whereas alate and alate nymphs (winged reproductive termites) can be very low in protein and high in fat (i.e.,  > 50% fat)38. Joshi et al.17 surmised that sloth bears consumed primarily termite eggs and defending soldiers based on the residues in bear feces and the absence of eggs and soldiers at termite mounds after sloth bear feeding bouts. Although not measured, the dry matter of termite eggs is likely high in both protein and fat, which would create a high fat ME because of the much greater energy content of fat than protein39. The high fruit diet of the summer will be low in protein and fat and high in carbohydrates if not supplemented with other fat-rich foods (e.g., grubs or insect larvae)17. Thus, depending on season and which stage of the ant and termite life cycle the bears consume, wild sloth bears could be consuming either high or low-protein or fat diets.The preference for fat that we observed differs markedly from current zoo diets. Zoo diets can be classified into two macronutrient types: 1) high carbohydrate, low protein, low fat diets that use grains, often in cooked porridges or soups, with fruits and vegetables or 2) diets having more modest or intermediate levels of protein, fat, and carbohydrates that include dog food, bear chows, or omnivore dry or canned products supplemented with fruits and vegetables (Fig. 3). Examples of the first type of diet are more common in Germany [e.g., Leipzig Zoo (ME protein 11%, fat 5%, and carbohydrate 84%)] and the various bear rescue centers in India [e.g., Bannerghatta Bear Rescue Centre (ME protein 10%, fat 9%, and carbohydrate 81%)]. Examples of the second type of diet are more common in US and other European zoos and have more protein and fat than the high grain diets but are much lower in fat than what bears selected in the current study22 (Fig. 3). Nevertheless, bears consuming all past and current zoo diets are prone to developing hepatobiliary cancer and inflammatory bowel disease.If these problems are dietary in origin and not due to something unique to feeding on termites and ants (e.g., development of a unique gastrointestinal microbiome or consumption of formic acid in ants or chitin in both ants and termites), there are two broad types of diets not fed in captivity (i.e., high protein diets and high fat diets) (Fig. 3). In evaluating if either one of those might be more suitable for sloth bears, the protein ME ratios of ursid milks and the diets voluntarily selected by brown bears, polar bears, giant pandas, and sloth bears are low and do not differ from each other (t(3) = 2.449, p = 0.092), which minimizes maintenance energy requirements and maximizes the efficiency of gain1,3,4,29,40 (Table 1). Additionally, brown bears and sloth bears prefer high fat, low carbohydrate diets when given a choice between foods rich in either carbohydrates or fats3 (Table 1, Fig. 3). This fat preference in the adult ursid diet is virtually identical to that occurring in ursid milks (t(2) = -0.726, p = 0.543) even though omnivorous ursids likely have a strong preference for sweet flavors41.While an understanding of the link between dietary macronutrient content and biliary cancer is lacking, we hypothesize that bears, such as polar bears and apparently sloth bears that prefer or evolved to consume high-fat diets, have high resting rates of bile production. Consequently, when sloth bears consume a high-carbohydrate, low-fat diet long term, bile is not secreted into the digestive tract as fast as it is being produced and may back up in the bile ducts, cause bile duct dilation and inflammation, and ultimately biliary cancer. An example of this process is a rare congenital disease in humans and other animals known as choledochal cyst disease. Sacs or outpocketings may develop along the bile ducts in this disease. Bile sitting in those sacs or in the bile ducts causes inflammation of the duct walls and, if not treated by surgical excision, biliary cancer42.If we assume the macronutrient characteristics of ursid milks and the preferences for low protein, low carbohydrate, high fat diets exhibited by brown bears, polar bears, and sloth bears are healthy, current and past sloth bear zoo diets have provided too little fat, too much digestible carbohydrate, and often too much protein (Fig. 3). While this mismatch between the diets fed in captivity and what sloth bears prefer might explain the high incidence of hepatobiliary cancer, inflammatory bowel disease, and poor reproduction world-wide, we cannot dismiss the possibility that the bears’ preference for avocados and fat and the avoidance of apples, baked yams, and digestible carbohydrates in the current study has nothing to do with their macronutrient content and would be unhealthy long-term. Thus, additional feeding studies are needed to determine if a high fat, low protein, low carbohydrate diet might be the key to improving the health, reproduction, and longevity of captive sloth bears.Finally, the selection of lower protein diets by giant pandas, polar bears, sloth bears, and brown bears and the often low-protein omnivorous diets of the other four ursids indicate that all ursids can modulate liver catabolic enzyme activity when needed to conserve protein. This would suggest that this ability to conserve protein occurred early in the evolution of ursids from a high protein carnivore ancestor and may have been critical to the spread of ursids world-wide by opening niches that could not be filled by another high protein carnivore. While all ursids at times may consume foods with a much higher protein content than that of a low protein omnivore, that selection process can only be evaluated relative to the other available dietary choices interacting with foraging and metabolic constraints and does not indicate their preferred diet is that of a high protein carnivore2,43,44. More

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Study area and soil propertiesA field experiment was established in May 2012 at Shenyang Agro-Ecological Station (41°31′N, 123°22′E) of the Institute of Applied Ecology, Chinese Academy of Sciences, Northeast China. This region has a warm-temperate continental monsoon climate. The mean annual air temperature and annual precipitation are 7.5 °C and 680 mm, respectively. The soil is classified as Luvisol (FAO classification). The soil properties of the topsoil layer (0–20 cm) at the start of the experiment are as follows: SOC = 9.0 g kg−1, available NH4+–N = 1.18 mg kg−1; available NO3−–N = 9.04 mg kg−1; Olsen-P = 38.50 mg kg−1, available K = 97.90 mg kg−1, bulk density = 1.25 g cm−3, and pH = 5.8. The determination method of soil was shown in “Soil analysis” section.Field experimentThree treatments were established in this experiment: (1) mineral fertilizers (NPK); (2) pig manure incorporation at a local conventional AM application rate of 15 Mg ha−1 yr−1 (NPKM, 126 kg N ha−1 on dry weight); and (3) NPKM plus DMPP (3,4-Dimethylpyrazole phosphate) incorporation at a rate of 0.5% of applied urea (2.39 kg ha−1, 220 kg N/the N content of urea (0.46) × 0.5%) (NPKI + M). The treatments were applied following a randomized design across three replicate field plots (4 m × 5 m). Plots of different treatments remained unchanged in the same locations for 4 years. Each year, the composted pig manure (213 g C kg−1 and 22 g N kg−1 based on dry weight on average, characteristics of pig manure was listed in Table S1) was broadcasted evenly onto the plots a few days before maize planting, and ploughed to a depth of 20 cm by machine (TG4, Huaxing, China). For the respective treatments, urea (220 kg N ha−1 yr−1), calcium superphosphate (110 kg P2O5 ha−1 yr−1), and potassium chloride (110 kg K2O ha−1 yr−1) were applied on the same day as maize (Zea mays L.) was planted. The urea and inhibitor were fully mixed before application.Maize (cultivar was Fuyou #9) was planted on 3rd May 2012, 3rd May 2013, 6th May 2014, and 10th May 2015, at a spacing of 37 cm and 60 cm between rows. No irrigation was applied throughout the experimental period. Maize was harvested on 13th September 2012, 29th September 2013, 29th September 2014, and 29th September 2015, respectively. At harvest, maize yield and aboveground biomass yield were measured by harvesting all plants (20 m2) in each plot. The straw and grain were removed after each harvest and the soil with about 5 cm maize stem was ploughed to a depth of approximately 20 cm in April each year.Each cropping cycle, therefore, consisted of periods of maize (from May to September) and fallow (from October to April) of the following year.The precipitation and air temperature data were acquired from the meteorological station of the Shenyang Agro-Ecological Station. The precipitation during the 2012/2013, 2013/2014, 2014/2015, and 2015/2016 periods were 911.9 mm, 621.7 mm, 485.7 mm, and 585.3 mm, respectively (Fig. 1). 72.3%, 75.5%, 66.5%, and 73.0% of these annual precipitations occurred during maize-growing period, respectively. The mean annual air temperatures in these years were 7.7 °C (− 21.2 to 27.5 °C), 8.1 °C (− 22.7 to 28.3 °C), 9.5 °C (− 21.7 to 28.2 °C) and 9.3 °C (− 17.1 to 27.0 °C), respectively. The soil temperature at a depth of 5 cm varied between − 14 and 35 °C during the four-year period (Fig. 2b). The change trend of soil surface temperature was the same as that of soil temperature at 5 cm depth (Fig. 2a). The mean soil WFPS (0–15 cm) varied between 15 and 73% (Fig. 2c).Figure 1Precipitation and daily mean air temperature during four annual cycles from May 2012 to April 2016 in the experimental field.Full size imageFigure 2Seasonal variations in soil temperature (at soil surface and 5 cm soil depth) and WFPS% at 0–15 cm depth from May 2012 to April 2016.Full size imageGas sampling and analysisThe gas was sampled between 3rd May 2012 and 14th April 2016 using a static closed chamber system as described by Dong et al.16. Briefly, a stainless-steel chamber base (56 cm length × 28 cm width) was inserted into the soil of each plot to a depth of approximately 10 cm, with its long edge perpendicular to the rows of maize. The top chamber (56 cm length × 28 cm width × 20 cm height) was also made of stainless steel. Gas samples were obtained using a syringe 0, 20, and 40 min after the chambers had been closed between 9:00 am and 11:00 am on each sampling day. Gas samples were collected every 2‒6 days and every 7‒15 days during the growing seasons and non-growing seasons, respectively. The first gas sampling time was on day 1, day 3, day 1, and day 3 after maize planting each year. The N2O concentrations in gas samples were quantified using a gas chromatograph (Agilent 7890A, Shanghai, China) with an electron capture detector.Soil analysisThe soil temperature and volumetric water content (SVWC) were measured at depth of 0–15 cm using a bent stem thermometer and a time-domain reflectometry (Zhongtian Devices Co. Ltd, China), respectively. SVWC was converted to soil water-filled pore space (WFPS) using the following equation:$${text{WFPS}} = {text{SVWC}}/(1{-}{text{BD}}/{text{particle}},{text{density}}),$$
(1)
where BD is soil bulk density (g cm−3). Particle density was assumed to be 2.65 g cm−3.Soil samples from the 0–20 cm layer were collected in each plot in April 2012 (before sowing) and October 2015 (maize harvest) using a 5 cm diameter stainless steel soil sampler. The five soil samples collected from different locations in each plot were mixed thoroughly. Visible roots were removed by hand and the samples were air-dried and sieved using a 0.15 mm sieve. SOC was then quantified using an elemental analyzer (Vario EL III, Elementar, Germany). Soil available NH4+–N and NO3−–N were extracted with 2 M KCl and measured colorimetrically using a continuous flow injection analyzer (Futura, Alliance, France)17. Soil Olsen-P was extracted with NaHCO3 and colorimetrically measured using a spectrophotometer (Lambda 2, PerkinElmer, USA). Soil available K was extracted by 1 M CH3COONH4 and analyzed with a flame photometer (FP640, Jingmi, China). Soil pH was determined with deionized water (1:2.5) and analyzed using a pH meter (PHS-3C, LeiCi, China) with a glass electrode.DNA extraction and real-time quantitative PCRThe soil samples for measuring the abundance of nitrification and denitrification functional genes were collected on May 20, 2015. Soil DNA was extracted with the soil DNA extracted kits (EZNA soil DNA Kit; Omega Bio-Tek Inc., U.S.A.). The copy numbers of nitrification and denitrification functional genes were determined by q-PCR with the Roche LightCyler® 96 (Roche, Switzerland). Additional details about the primers and amplification procedure can be found in Dong et al.16.Data analysisThe N2O flux (μg N2O–N m−2 h−1) is calculated based on the increase of N2O concentration per unit chamber area for a specific time interval18 as follows:$${text{F}} = 273/left( {273 + {text{T}}} right) times {text{M}}/22.4 times {text{H}} times {text{dc}}/{text{dt}} times 1000$$
(2)
where F (μg N2O–N m−2 h−1) is the N2O flux, T (◦C) is the air temperature in the chamber, M (g N2O–N mol−1) is the molecular weight of N2O–N, 22.4 (L mol−1) is the molecular volume of the gas at 101.325 kPa and 273 K, H (m) is the chamber height, dc/dt (ppb h−1) is the rate of change in the N2O concentration in the chamber.Cumulative N2O emissions were calculated as follows:$${text{Cumulative}},{text{emission}} = mathop sum limits_{{{text{i}} = 1}}^{{text{n}}} frac{{({text{F}}_{{text{i}}} + {text{F}}_{i + 1} )}}{2} times ({text{t}}_{{{text{i}} + 1}} – {text{t}}_{{text{i}}} ) times 24$$
(3)
where F is the N2O emission flux (μg N2O–N m−2 h−1), i is the ith measurement, (ti+1 − ti) is the number of days between two adjacent measurements, and n is the total number of the measurements. Annual N2O emissions were calculated between the fertilization dates of each successive year.The SOC stock (Mg ha−1) in the topsoil was calculated as:$${text{C}}_{{{text{stock}}}} = {text{SOC}} times {text{BD}} times {text{D}} times 10,$$
(4)
where BD is soil bulk density (g cm−3), D is the depth of the topsoil (0.2 m).The topsoil SOC sequestration rate (SOCSR) (Mg ha−1 yr−1) was estimated using the following equation:$${text{SOCSR}} = left( {{text{C}}_{{{text{stock2015}}}} – {text{C}}_{{{text{stock2012}}}} } right) times {text{t}}^{ – 1} ,$$
(5)
where Cstock2015 and Cstock2012 are the SOC stocks in 2015 and 2012, respectively, and t is the duration of the experiment (years).Statistical analyses were performed using SPSS 13.0 (SPSS, Chicago, USA). The differences in cumulative N2O emissions and maize yields within a year, and other factors among treatments were assessed using one-way Analysis of Variance (ANOVA) with least significant difference post-hoc tests and a 95% confidence limit. The effects of different treatments, years, and their interactions on N2O emission, maize yield and aboveground biomass were examined using one-way repeated measures ANOVA. Pearson correlation analysis was used to analyze the relationships between cumulative N2O emissions and precipitation (N = 12 (three data each year, four years)), as well as N2O flux and soil available nitrogen content.
Statements of research involving plantsIt is stated that the current research on the plants comply with the relevant institutional, national, and international guidelines and legislation. It is also stated that the appropriate permissions have been taken wherever necessary, for collection of plant or seed specimens. It is also stated that the authors comply with the ‘IUCN Policy Statement on Research Involving Species at Risk of Extinction’ and the ‘Convention on the Trade in Endangered Species of Wild Fauna and Flora’. More