Growing Season Of Maize Research Articles

Greenhouse gas emissions from the growing season are regulated by precipitation events in conservation tillage farmland ecosystems of Northeast China

Reducing greenhouse gas (GHG) emissions from agricultural ecosystems is vital to mitigate global warming. Conservation tillage is widely used in farmland management to improve soil quality; however, its effects on soil GHG emissions remain poorly understood, particularly in high-yield areas. Therefore, our study aimed to evaluate the effects of no-tillage (NT) combined with four straw-mulching levels (0 %, 33 %, 67 %, and 100 %) on GHG emission risk and the main influencing factors. We conducted in-situ observations of GHG emissions from soils under different management practices during the maize-growing season in Northeastern China. The results showed that NT0 (705.94 g m−2) reduced CO2 emissions by 18 % compared to ridge tillage (RT, 837.04 g m−2). Different straw mulching levels stimulated N2O emissions after rainfall, particularly under NT combined with 100 % straw mulching (2.89 kg ha−1), which was 45 % higher than that in any other treatments. The CH4 emissions flux among different treatments was nearly zero. Overall, straw mulching levels had no significant effect on the GHG emissions. During the growing season, soil NH4+-N (< 20 mg kg−1) remained low and decreased with the extension of growth stage, whereas soil NO3−-N initially increased and then decreased. More importantly, the results of structural equation modeling indicate that: a) organic material input and soil moisture are key factors affecting CO2 emissions, b) nitrogen fertilizer and soil moisture promote N2O emissions, and c) climatic factors exert an inexorable influence on the GHG emissions process. Our conclusions emphasize the necessity of incorporating precipitation-response measures into farmland management to reduce the risk of GHG emissions.

Effects of drip irrigation coupled with controlled release potassium fertilizer on maize growth and soil properties

Drip irrigation and the application of controlled-release fertilizers are two effective and important technical measures to conserve water and fertilizer resources and promote the growth and development of maize. However, the underlying physiological mechanism of how drip irrigation combined with controlled release potassium chloride affects maize production and soil properties remains unknown. In different soil water conditions, pot experiments were conducted during the summer maize growing season in 2022 and 2023 to measure maize grain yield, irrigation water productivity, apparent recovery efficiency of potassium, plant physiological characteristics, and soil enzyme activities under the coupling of two irrigation methods (flood irrigation and drip irrigation) and three potassium fertilizer types (potassium chloride (KCl), controlled release potassium chloride (CRK), and 70 % CRK mixed with 30 % KCl). The results revealed that drip irrigation had 6.5–8.1 % and 6.7–9.4 % higher average soil volumetric water content and 3.8–8.1 % and 4.5–13.0 % higher irrigation water productivity than flood irrigation in the two maize growth seasons, respectively. Under the drought conditions, the treatments of 70 % CRK-30 % KCl application led to a significant increase in maize yield by 9.5–10.7 % and 12.2–16.8 % and apparent recovery efficiency of potassium by 8.3–14.2 % and 10.7–10.8 % compared to CRK application treatments in 2022 and 2023, respectively. Compared with the other treatments, the treatment of drip irrigation coupled with 70 % CRK-30 % KCl application under drought conditions (DIDMK) resulted in 4.5–28.7 % and 1.1–31.9 % higher grain yield, 8.2–64.4 % and 8.6–66.8 % higher irrigation water productivity, and 5.9–28.0 % and 6.7–18.9 % higher apparent recovery efficiency of potassium in 2022 and 2023, respectively. Meanwhile, the leaf net photosynthetic rate, rubisco, catalase, peroxidase, and soil enzyme activities of DIDMK treatment were also maintained at a high level. Our results indicated that drip irrigation coupled with 70 % CRK-30 % KCl application were the optimal water and potassium supply modes for maize production. This study can provide useful information regarding summer maize sustainable production and provide theoretical and technical support for summer maize irrigation and potassium fertilizer application technologies in water-scarce regions of China and other similar regions around the world.

Optimizing sowing dates increase solar radiation to mitigate maize lodging and yield variability: A five-year field study

Optimizing sowing dates (SDs) is a potential strategy for adjusting maize production to climate change and increasing yield. However, there is still a lack of research on the combined effects of lodging and yield in relation to climatic variables across various SDs. This study aims to investigate the changing patterns and distribution of important climatic variables during the maize growth season, their impact on yield and lodging, and the critical factors affecting lodging at crucial growth stages under different SD scenarios. In this study, we assessed the impact of climate change on yield and lodging by conducting field experiments over 5 years (2015, 2016, 2019–2021) encompassing 25 SDs in the Sichuan basin, China. Results demonstrated that the lodging rate had a significant effect on the coefficient of variation (CV, 3.31–10.50%) of maize yield. A 1% increase in lodging rate, led to a decrease of 58.05 kg ha-1 in yield. Changes in SDs notably affected solar radiation (Sr) from emergence to silking (E-R1). Additionally, the study found that Sr explained 34.7% of the lodging rate variation in E-R1. Analysis of historical meteorological data showed notable inter-annual variations in Sr trends, with a decline of -8.7763 MJ m-2 yr-1 from 1990 to 2021, especially noticeable from late May to early July. Variation Partitioning Analysis (VPA) revealed that climatic variables during the period from emergence to physiological maturity (E-R6) and E-R1 explained 43.9 and 53.2% of yield, respectively, across different SDs. These variables also contributed 56.0 and 45.4% to lodging. Using Random Forest (RF) determined that changes in SDs significantly impacted lodging rates mainly through modifications in basal internode morphology, which explained 69.79% of the variation. The study identified optimal sowing dates for achieving high and consistent yields, primarily occurring between late March and mid-April, attributed to increased Sr during E-R1. Overall, this research provides valuable insights into the effects of climate change on stalk lodging and offers guidance on adjusting sowing dates to mitigate maize lodging rates.

Projected Increase in Compound Drought and Hot Days over Global Maize Areas under Global Warming

Compound drought and hot events can lead to detrimental impacts on crop yield with grave implications for global and regional food security. Hence, an understanding of how such events will change under unabated global warming is helpful to avoid associated negative impacts and better prepare for them. In this article, we comprehensively analyze the projected changes in compound drought and hot days (CDHDs) occurring within the maize-growing season of 2015–2100 over dynamic global maize areas using 10 downscaled Coupled Model Intercomparison Project Phase 6 (CMIP6) models and four socio-economic scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5). The results demonstrate a notable increase in the frequency and severity of CDHDs over global maize areas under all four SSPs, of which SSP5-8.5 has the fastest rise, followed by SSP3-7.0, SSP2-4.5 and SSP1-2.6. By the end of 21st century, the global average frequency and severity of CDHDs will reach 18~68 days and 1.0~2.6. Hotspot regions for CDHDs are mainly found in southern Africa, eastern South America, southern Europe and the eastern USA, where drought and heat show the most widespread increases. The increase in CDHDs will be faster than general hot days so that almost all increments of hot days will be accompanied by droughts in the future; therefore, compound dry and hot stresses will gradually become the predominant form of dry and heat stress on maize growth. The results can be applied to optimize adaptation strategies for mitigating risks from CDHDs on maize production worldwide.

CLIM4OMICS: a geospatially comprehensive climate and multi-OMICS database for maize phenotype predictability in the United States and Canada

Abstract. The performance of numerical, statistical, and data-driven diagnostic and predictive crop production modeling relies heavily on data quality for input and calibration or validation processes. This study presents a comprehensive database and the analytics used to consolidate it as a homogeneous, consistent, multidimensional genotype, phenotypic, and environmental database for maize phenotype modeling, diagnostics, and prediction. The data used are obtained from the Genomes to Fields (G2F) initiative, which provides multiyear genomic (G), environmental (E), and phenotypic (P) datasets that can be used to train and test crop growth models to understand the genotype by environment (GxE) interaction phenomenon. A particular advantage of the G2F database is its diverse set of maize genotype DNA sequences (G2F-G), phenotypic measurements (G2F-P), station-based environmental time series (mainly climatic data) observations collected during the maize-growing season (G2F-E), and metadata for each field trial (G2F-M) across the United States (US), the province of Ontario in Canada, and the state of Lower Saxony in Germany. The construction of this comprehensive climate and genomic database incorporates the analytics for data quality control (QC) and consistency control (CC) to consolidate the digital representation of geospatially distributed environmental and genomic data required for phenotype predictive analytics and modeling of the GxE interaction. The two-phase QC–CC preprocessing algorithm also includes a module to estimate environmental uncertainties. Generally, this data pipeline collects raw files, checks their formats, corrects data structures, and identifies and cures or imputes missing data. This pipeline uses machine-learning techniques to fill the environmental time series gaps, quantifies the uncertainty introduced by using other data sources for gap imputation in G2F-E, discards the missing values in G2F-P, and removes rare variants in G2F-G. Finally, an integrated and enhanced multidimensional database was generated. The analytics for improving the G2F database and the improved database called Climate for OMICS (CLIM4OMICS) follow findability, accessibility, interoperability, and reusability (FAIR) principles, and all data and codes are available at https://doi.org/10.5281/zenodo.8002909 (Aslam et al., 2023a) and https://doi.org/10.5281/zenodo.8161662 (Aslam et al., 2023b), respectively.

Estimating Maize Yield from 2001 to 2019 in the North China Plain Using a Satellite-Based Method

Maize is one of the main food crops and is widely planted in China; however, it is difficult to get timely and precise information on yields. Because of the benefits of remote sensing technology, satellite-based models (e.g., eddy covariance light use efficiency, EC-LUE) have a lot of potential for monitoring crop productivity. In this study, the gross primary productivity (GPP) of maize in the NCP was estimated using the EC-LUE model, and the GPP was subsequently transformed into yield using the harvest index. Specifically accounting for the spatiotemporal variation in the harvest index, the statistical yield and estimated GPP from the previous year were used to generate region-specific harvest indexes at the county scale. The model’s performance was assessed using statistical yield data. The results demonstrate that the increase in the total GPP in the summer maize-growing season in the NCP is directly related to the increase in the planting area, and the harvest index has significant heterogeneity in space, and the fluctuation in time is small, and the estimated yield can simulate 64% and 55%, respectively, of the variability in the yield at the county and city scales. The model also accurately captures the inter-annual changes in yield (the average absolute percentage errors are less than 20% for almost all years), but model performance varies by region. It performs better in continuous areas of maize-growing. The results from this study demonstrate that the EC-LUE model can be applied to estimate the yield from a variety of crops (other than winter wheat) and that it can be used in conjunction with a region-specific harvest index to track the production of large-scale crops.

Effects of Plant Growth Regulators and Nitrogen Management on Root Lodging Resistance and Grain Yield under High-Density Maize Crops

Lodging is one of the main factors causing yield loss of maize under high-density planting conditions. Root lodging as an important lodging type has received little attention. Plant growth regulators (PGRs) and nitrogen fertilizer can coordinate the relationship between root lodging and yield. This two-year field experiment was conducted with two nitrogen levels of N225 (225 kg ha−1) and N300 (300 kg ha−1) at a high planting density (90,000 plants ha−1) during the maize growth season from 2019 to 2020. Plant growth regulator (Yuhuangjin, the mixture of 3% DTA-6 and 27% ethephon) was sprayed at the V8 stage. The results showed that PGRs significantly decreased plant height, improved root distribution and dry weight, enhanced photosynthetic rate and activities of photosynthetic carboxylase in ear leaves, and improved root bleeding sap and root activities after the silking stage. N225 combined with PGRs reduced the occurrence of root lodging and was conducive to photosynthate accumulation and root nutrient supply; it coordinated root regulation and morphological and physiological shoot functions, and played a crucial role in reducing root lodging and improving maize yield.

Molecular identification and phylogenetic characterization of A-strain isolates of maize streak virus from western Ethiopia.

The A-strain of maize streak virus (MSV) causes maize streak disease (MSD), which is a major biotic threat to maize production in sub-Saharan Africa. Previous studies have described different MSV strains of economic importance from southern and eastern African countries and how eastern African regions are hubs for MSV diversification. Despite these efforts, due to a lack of extensive sampling, there is limited knowledge about the MSV-A diversity in Ethiopia. Here, field sampling of maize plants and wild grasses with visible MSD symptoms was carried out in the western Ethiopian regions of Gambela, Oromia, and Benishangul-Gumuz during the maize-growing season of 2019. The complete genomes of MSV isolates (n = 60) were cloned and sequenced by the Sanger method. We used a model-based phylogenetic approach to analyse 725 full MSV genome sequences available in the GenBank database together with newly determined genome sequences from Ethiopia to determine their subtypes and identify recombinant lineages. Of the 127 fields accessed, MSD prevalence was highest, at 96%, in the Gambela region and lowest in Oromia, at 66%. The highest mean symptom severity of 4/5 (where 5 is the highest and 1 the lowest) was observed in Gambela and Benishangul-Gumuz. Our results show that these newly determined MSV isolates belong to recombinant lineage V of the A1 subtype, with the widest dissemination and greatest economic significance in sub-Saharan Africa and the adjacent Indian Ocean islands.

Spatiotemporal Changes in Precipitation during the Summer Maize Growing Season in the North China Plain and Analysis of Its Causes

The North China Plain is an important summer maize production region in China. Investigating spatiotemporal variation patterns of precipitation during the summer maize growing season will guide the prevention of droughts and floods and ensure food production. Daily precipitation data during the summer maize growing season in the North China Plain from 1960–2020 were used to analyze spatiotemporal changes in precipitation, examine the migration patterns of precipitation barycenters, and quantitatively analyze the effects of ENSO (El Niño-Southern Oscillation) warm and cold events on precipitation variation characteristics. Results revealed that in the past 61 years, precipitation showed an insignificant decreasing trend; however, there were considerable differences detected in the spatial distribution layouts of precipitation between different developmental stages. The precipitation distribution layout during the sowing–jointing stage was mainly “North–South”, the zero contour was near 36° N, and the other developmental stages were mainly “global” with phases that were the opposite of one another. Moreover, the precipitation barycenter during the jointing–flowering stage showed a significant southward migration. Precipitation during the three developmental stages negatively correlated with warm events, precipitation during the flowering–maturation stage positively correlated with cold events, the relationship between precipitation changes during warm and cold events and the intensity of warm and cold events was not significant, and Pacific Decadal Oscillation (PDO) was the main climatic factor that affected precipitation changes during the summer maize-growing season in the North China Plain.

Responses of soil respiration and C sequestration efficiency to biochar amendment in maize field of Northeast China

The application of biochar in agricultural ecosystems is a viable strategy for mitigating climate change and ensuring food security. Northeast China is China’s main maize-growing region, as well as the country's greatest commodity grain base. However, due to a lack of long-term field investigations, information on the effects of biochar on soil CO2 emissions and C sequestration in brown soil in Northeast China is restricted. A 3-year field experiment was conducted in Northeast China with biochar applied at 0 (CK), 15.75 (BC1), 31.5 (BC2), and 47.25 (BC3) t ha−1 rates to explore the continuous impacts on soil CO2 emissions, soil enzyme activities, humic substance (HS) composition, and C sequestration efficiency (CSE). The soil CO2-C emissions in biochar treatments were significantly higher (18.04–73.15 %) than in the control during the maize-growing season. Biochar amendment induced a significant increase in soil urease, sucrase, catalase, and β-glucosidase activities and a significant decrease in bulk density (BD). Furthermore, significant positive correlations were observed between soil CO2 emissions and the four enzymes mentioned above. The C content of humin (HU) increased dramatically as the rate of biochar use increased, whereas the C content of humic acid (HA) and fulvic acid (FA) remained unchanged. The soil C storage levels in the 0–60 cm soil depth in BC1, BC2, and BC3 were 13.30 %, 24.41 %, and 39.06 % higher than CK. The CSE ranged from 80.4 % to 87.6 %, depending on biochar addition rates. To conclude, the present study suggested that maize stover biochar incorporation had a great potential for improving soil C sequestration to agricultural soils of Northeast China, even though biochar addition enhanced soil respiration and enzyme activities.

Potential Nitrogen Mineralization and Its Availability in Response to Long-Term Fertilization in a Chinese Fluvo-Aquic Soil

The determination of organic nitrogen (N) mineralization is crucial for estimating N availability, quantifying exogenous inputs, and estimating associated environmental impacts. The objective of this study was to explore the effect of long-term various fertilization on soil organic N mineralization potential (NMP), which influences plant N accessibility. Treatments from a 26-year long-term field experiment with no fertilization (CK), chemical fertilizer N at 165 kg N ha−1 and P at 82.5 kg P2O5 ha−1 (NP), NP with K fertilizer at 165, 82.5, 82.5 kg ha−1 N, P2O5 and K2O (NPK), NPK at 165, 82.5, 82.5 kg ha−1 N, P2O5 and K2O with manure at 7857.14 kg ha−1 (NPKM), and NPKM at 165, 82.5, 82.5 kg ha−1 N, P2O5 and K2O with manure at 1.5× application rate (11,785.71 kg ha−1) (1.5NPKM) were examined for potentially mineralizable N by aerobic incubation at 35 °C for 30 weeks. Three pools (Pools I, II, and III) of mineralizable N were recognized. Pool I, the mineralization flush on rewetting in the first 2 weeks; Pool II, gross N mineralization between weeks 2 and 30; and Pool III, the potentially mineralizable N, predicted from the fitted curve, that did not mineralize during the incubation period. Soil microbial biomass carbon (SMBC) and N (SMBN) as well as fixed ammonium (NH4+) contents and relationship with N mineralization rate (k) were also studied. Long-term manure application yielded a significantly higher k (0.32 week−1) than other treatments (0.12–0.22 week−1) but not a significantly higher NMP. Nitrogen mineralization during the wheat and maize-growing seasons was predicted to be 8.7–26.3 (mg N kg−1 soil) and 25.9–42.1 (mg N kg−1 soil), respectively. Both labile mineralizable N pools (Pools I and II) followed the same patterns in the treatments: 1.5NPKM &gt; NPKM &gt; NPK &gt; NP &gt; CK, while the reverse was true for stable N (Pool III). The significant positive correlation between k with SMBC and SMBN (R2 = 0.93, p = 0.008 and R2 = 0.94, p = 0.006) suggested that the higher mineralization rate might be contributed by the higher soil microbial biomass in NPKM. The trends of fixed NH4+ and mineralized N were coupled. Long-term manure application significantly improved the N mineralization rate in soil. Manure application is an effective strategy to enhance soil microbial biomass and soil N availability and has the potential to reduce the dependence upon chemical N fertilization.

A Proposed Satellite-Based Crop Insurance System for Smallholder Maize Farming

Crop farming in Sub-Saharan Africa is constantly confronted by extreme weather events. Researchers have been striving to develop different tools that can be used to reduce the impacts of adverse weather on agriculture. Index-based crop insurance (IBCI) has emerged to be one of the tools that could potentially hedge farmers against weather-related risks. However, IBCI is still constrained by poor product design and basis risk. This study complements the efforts to improve IBCI design by evaluating the performances of the Tropical Applications of Meteorology using SATellite data and ground-based observations (TAMSAT) and Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) in estimating rainfall at different spatial scales over the maize-growing season in a smallholder farming area in South Africa. Results show that CHIRPS outperforms TAMSAT and produces better results at 20-day and monthly time steps. The study then uses CHIRPS and a crop water requirements (CWR) model to derive IBCI thresholds and an IBCI payout model. Results of CWR modeling show that this proposed IBCI system can cover the development, mid-season, and late-season stages of maize growth in the study area. The study then uses this information to calculate the weight, trigger, exit, and tick for each of these growth stages. Although this approach is premised on the prevailing conditions in the study area, it can be applied in other areas with different growing conditions to improve IBCI design.

Straw Incorporation Effects on Net Photosynthetic Carbon Assimilation and Maize Growth

Returning straw into soil could increase soil organic carbon (SOC) and promote crop growth. However, little has been reported on the source of C for increased SOC (straw C or crop photosynthetic C). To investigate the assimilation of photosynthetic C and its distribution in soil in the maize growth season, we set up a 1-year 13C pulse-labeling experiment in a consecutive maize-straw-returning long-term trial. Four treatments were included: no straw return (control), straw mulching on the soil surface (cover), return in 0–20 cm layer (shallow), and 20–40 cm layer (deep). We found that the deep straw incorporation significantly (P &amp;lt; 0.05) increased maize 100-grain weight (by 7.8%), yield in the coming year (by 10.5%), and SOC (by 13.4%) compared with the control. During the growing season, the deep straw incorporation increased photosynthetic 13C assimilation in shoots by 17.4% and the partitioning of photosynthetic 13C to soil by 7.9% at early jointing, and by 11.5% at maturity. The contribution of photosynthetic C to microbial biomass C (MBC) and dissolved organic C (DOC) was highest at jointing, and at harvest amounted to 39.1 % of MBC and 28.8% of DOC. The results highlighted the importance of regulating the soil carbon dynamics via the deep straw return strategy. In conclusion, deep straw incorporation significantly increased photosynthetic efficiency and facilitated partitioning of photosynthetic C to roots and soil, thus promoting maize growth and yield.

Do Satellite Data Correlate with In Situ Rainfall and Smallholder Crop Yields? Implications for Crop Insurance

Adverse weather is one of the most prevalent sources of risk in agriculture. Its impacts are aggravated by the lack of effective risk management mechanisms. That is why resource-poor farmers tend to respond to weather risks by adopting low-capital investment, low-return, and low-risk agricultural practices. This challenge needs to be addressed with innovative risk management strategies. One of the tools that is gaining traction, especially in the developing countries, is weather-index-based insurance (WII). However, WII uptake is still low because of several constraints, one of which is basis risk. This study attempts to address this problem by evaluating the suitability of TAMSAT, CHIRPS, MODIS, and Sentinel-2 data for WII. We evaluated the first three datasets against in situ rainfall measurements at different spatial and temporal scales over the maize-growing season in a smallholder farming area in South Africa. CHIRPS had higher correlations with in situ measured rainfall data than TAMSAT and MODIS NDVI. CHIRPS performed equally well at 10 km and 25 km spatial scales, and better at monthly than daily and 16-day time steps (maximum R = 0.78, mean R = 0.72). Due to the lack of reliable historical yield data, we conducted yield surveys over three consecutive seasons using an objective crop cut method. We then assessed how well rainfall and NDVI related with maize yield. There was a poor relationship between these variables and maize yield (R2 ≤ 0.14). The study concludes by pointing out that crop yield does not always have a linear relationship with weather and vegetation indices, and that water is not always the main yield-limiting factor in smallholder farming systems. To minimize basis risk, the process of designing WII must include identification of main yield-limiting factors for specific localities. Alternatively, insurers could use crop water requirement methods to design WII.

Evaluation and Projection of Diurnal Temperature Range in Maize Cultivation Areas in China Based on CMIP6 Models

The diurnal temperature range (DTR) is an important meteorological component affecting maize yield. The accuracy of climate models simulating DTR directly affects the projection of maize production. We evaluate the ability of 26 Coupled Model Intercomparison Project phase 6 (CMIP6) models to simulate DTR during 1961–2014 in maize cultivation areas with the observation (CN05.1), and project DTR under different shared socioeconomic pathway (SSP) scenarios. The root mean square error (RMSE), standard deviation (SD), Kling-Gupta efficiency (KGE) and comprehensive rating index (CRI) are used in the evaluation of the optimal model. The results show that CMIP6 models can generally reproduce the spatial distribution. The reproducibility of the annual average DTR in the maize cultivation areas is better than that in China but lower for the maize-growing season. The optimal model (EC-Earth3-Veg-LR) is used in the projection. Under the two SSPs, the DTR decreases compared with the historical period, especially in Northwest and North China. The DTR under SSP245 remains unchanged (annual) or increases slightly (growing season) during 2015–2050, while a significant decreasing trend is observed under SSP585. This highlights the importance of evaluating DTR in maize cultivation areas, which is helpful to further improve the accuracy of maize yield prediction.

Impact of an Exotic Invasive Pest, Spodoptera frugiperda (Lepidoptera: Noctuidae), on Resident Communities of Pest and Natural Enemies in Maize Fields in Kenya

The interactions among insect communities influence the composition of pest complexes that attack crops and, in parallel, their natural enemies, which regulate their abundance. The lepidopteran stemborers have been the major maize pests in Kenya. Their population has been regulated by natural enemies, mostly parasitoids, some of which have been used for biological control. It is not known how a new exotic invasive species, such as the fall armyworm (FAW), Spodoptera frugiperda (Lepidoptera, Noctuidae), may affect the abundance and parasitism of the resident stemborers. For this reason, pest and parasitism surveys have been conducted, before and after the FAW invaded Kenya, in maize fields in 40 localities across 6 agroecological zones (AEZs) during the maize-growing season, as well as at 3 different plant growth stages (pre-tasseling, reproductive, and senescence stages) in 2 elevations at mid-altitude, where all maize stemborer species used to occur together. Results indicated that the introduction of the FAW significantly correlated with the reduction of the abundance of the resident communities of maize stemborers and parasitoids in maize fields; moreover, the decrease of stemborer density after the arrival of FAW occurred mostly at both reproductive and senescent maize stages. It also suggests a possible displacement of stemborers by FAW elsewhere; for example, to other cereals. However, since this study was conducted only three years after the introduction of the FAW, further studies will need to be conducted to confirm such displacements.

Effects of Biochar and Straw on Soil N2O Emission from a Wheat Maize Rotation System

The effects of biochar and straw return on soil N2O emissions were studied in the winter wheat-summer maize rotation system of intensively farmed land in North China to provide a theoretical basis for N2O emission reduction and the efficient straw utilization. The experiment included the following four treatments:① Control (CK); ② Biochar application at a rate of 9.0 t·(hm2·a)-1 (C); ③ Straw return (SR); and ④ Straw return plus biochar application at a rate of 9.0 t·(hm2·a)-1 (C+SR). The results showed that in the wheat season, the CK treatment showed a slight decrease in soil N2O emission while the SR and C+SR treatments promoted soil N2O emission by 47.4% and 71.8%, respectively. In the maize-growing season, the CK treatment reduced soil N2O emission by 29.8% while the SR and C+SR treatments increased soil N2O emission by 13.4% and 35.8%, respectively. During the wheat-growing season, the soil water, NH4+-N, and MBN content were the main environmental factors affecting N2O emissions; during the maize-growing season, NO3--N, NH4+-N, and MBC content were the main environmental factors affecting emissions. Based on our results, the application of biochar to cropland is an effective option for mitigating greenhouse gas emissions, whereas direct straw return to fields might not be an effective strategy. More research is now needed to examine the effect of the return of straw of different maturity on N2O emissions.

Variability and controls of soil CO2 fluxes under different tillage and crop residue managements in a wheat-maize double-cropping system.

The spatial and temporal variability of soil CO2 emissions from agricultural soils is inherently high. While tillage and crop residue practices play vital roles in governing soil CO2 emission, their effects on the variability of soil CO2 fluxes across depths and seasons are still poorly understood. To address this, an experiment consisting of four treatments, namely conventional tillage with (CT+) and without crop residue application (CT-), as well as no tillage with (NT+) and without crop residue application (NT-), was conducted to investigate soil CO2 fluxes at top 40 cm soils with 10-cm depth intervals in a winter wheat-summer maize rotation system in the North China Plain. Our results showed soil CO2 fluxes increased with depth in both the wheat- and maize-growing seasons. However, the dominant factors in regulating soil CO2 fluxes changed with soil depth and seasons. In the wheat-growing season, increase in soil CO2 fluxes with depth was attributed to the increase of dissolved organic carbon-to-nitrogen ratio (DOC/DON) and a decline in soil DON concentration along the soil profile. These factors explained about 55-96% of the total variation in soil CO2 fluxes at different soil depths. In the maize-growing season, the dominant factors were soil DOC/DON ratio, soil DON concentrations, and soil moisture. These factors explained approximately 79-96% of the total variation in soil CO2 fluxes along the soil depth. Greater soil CO2 fluxes (except at 30-40 cm depth) were observed in NT- than CT- treatments. Furthermore, crop residue application enhanced soil CO2 fluxes across different depths, but the enhancement was more prominent in CT+ than NT+. Moreover, soil CO2 fluxes in the maize-growing season were greater than those in the wheat-growing season. Our results demonstrate that the effects of tillage regimes and crop residue management practices on soil CO2 emissions are not confined only to the plough layer but can extend to soils of over 30 cm depths. We also need to revisit the general conventional view that no tillage can significantly reduce soil CO2 emissions compared with conventional tillage for better climate change mitigation.

Safeguarding Food Supply and Groundwater Safety for Maize Production in China.

Quantifying sustainable nitrogen (N) management at the national scale is critical for developing targeted policies and strategies to simultaneously achieve food security and groundwater protection. In this study, we report county-scale optimization scenarios for Chinese maize production and evaluate their outcomes for safeguarding food supply and groundwater safety. First, we performed random forest regression modeling to simulate in situ NO3- leaching based on a meta-analysis that integrates climate, soil, water, and N balance parameters. The NO3- leaching was then mapped for 1406 counties based on data compiled from 2.89 million farmer surveys. Average NO3- leaching during the maize growth season was estimated to be 27.6 kg N ha-1, and 56% of counties had groundwater whose nitrate concentrations exceeded drinking water safety levels during 2005-2014. The top 5% farmers in each county produced not only more grain but also greater NO3- leaching. Scenario analysis of potential management changes found that when these top producers combined optimal N management practices, national N use in Chinese maize system was reduced by 25%, from 9.1 to 6.9 Mt, while maize production increased by 6.1%. Modeled NO3- leaching was 0.58 Mt, which was 31% lower than groundwater safety levels and 53% lower than the current leaching amount. This study provides evidence that integrated crop and N management practices implemented at the county level safeguard both maize crop food security and enhance environment sustainability.

Carbon dioxide fluxes in a farmland ecosystem of the southern Chinese Loess Plateau measured using a chamber-based method.

BackgroundFarmland accounts for a relatively large fraction of the world’s vegetation cover, and the quantification of carbon fluxes over farmland is critical for understanding regional carbon budgets. The carbon cycle of farmland ecosystems has become a focus of global research in the field of carbon dynamics and cycling. The objectives of this study are to monitor the temporal variation in the net ecosystem exchange (NEE) and soil respiration in a spring maize (Zea mays L.) farmland ecosystem of the southern Loess Plateau of China.MethodsA fully automated temperature-controlled flux chamber system was adopted in this study. The system contained nine chambers for CO2 flux measurements, and three treatments were conducted: with and without maize plants in the chamber, as well as a bare field. Observations were conducted from June to September 2011. This time period covers the seedling, jointing, heading, grain filling, and ripening stages of spring maize. Other factors, such as air temperature (Ta), soil temperature (Ts), soil water content (SWC), photosynthetically active radiation (PAR), and precipitation (P), were simultaneously monitored.ResultsThere was observed diurnal variation in the NEE of the maize ecosystem (NEE-maize). A short “noon break” occurred when the PAR intensity was at its maximum, while soil respiration rates had curves with a single peak. During the overall maize growth season, the total NEE-maize was –68.61 g C m−2, and the soil respiration from the maize field (SR-maize) and bare field (SR-bare field) were 245.69 g C m−2 and 114.08 g C m−2, respectively. The temperature sensitivity of soil respiration in the maize field exceeded that in the bare field. Significant negative correlations were found between the NEE, PAR, and temperature (all p-values < 0.01), with both Ta and PAR being the primary factors that affected the CO2 fluxes, collectively contributing 61.7%, 37.2%, and 56.8% to the NEE-maize, SR-maize, and SR-bare field, respectively. It was therefore concluded that both meteorological factors and farming practices have an important impact on the carbon balance process in corn farmland ecosystems. However, it is necessary to conduct long-term observational studies, in order to get a better understanding of the driving mechanism.