Project description:This study investigates the effects of sensor-based, variable-rate mineral nitrogen (N) application (VRA) in winter wheat (Triticum aestivum L.) on the spatial variability of grain yield, protein content, N uptake, N balance, and N efficiency compared with uniform N application (UA). To analyze the effects of VRA and UA on yield and N balance parameters, on-farm strip trials were conducted on heterogeneous arable fields covering an area of 49 hectares. The trials were carried out over a four-year period, from 2020 to 2023, with crops under both application methods placed in strips side-by-side. The N fertilizer requirements for growth stages (GSs) 32 and 39 were determined using an online map-overlay VRA method. This method integrated the site-specific yield potential and current plant development derived from spectral reflectance measurements using a tractor-mounted sensor system. The results show that the application of N fertilizer can be reduced by up to 38 kg ha-1 yr-1. The N efficiency can be increased by 15% and a significant reduction in variability of N balances can be achieved. However, the effects on yield and N efficiency are highly dependent on the specific application conditions (weather conditions, disease occurrence, and crop development). Not every field trial showed advantages of VRA over UA fertilization. Overall, the VRA system demonstrated encouraging potential, functioning as intended. However, further adjustment and optimization are required to ensure that the VRA fertilization system works robustly and reliably under on-farm conditions.
Project description:Improved prediction of optimal N fertilizer rates for corn (Zea mays L.) can reduce N losses and increase profits. We tested the ability of the Agricultural Production Systems sIMulator (APSIM) to simulate corn and soybean (Glycine max L.) yields, the economic optimum N rate (EONR) using a 16-year field-experiment dataset from central Iowa, USA that included two crop sequences (continuous corn and soybean-corn) and five N fertilizer rates (0, 67, 134, 201, and 268 kg N ha-1) applied to corn. Our objectives were to: (a) quantify model prediction accuracy before and after calibration, and report calibration steps; (b) compare crop model-based techniques in estimating optimal N rate for corn; and (c) utilize the calibrated model to explain factors causing year to year variability in yield and optimal N. Results indicated that the model simulated well long-term crop yields response to N (relative root mean square error, RRMSE of 19.6% before and 12.3% after calibration), which provided strong evidence that important soil and crop processes were accounted for in the model. The prediction of EONR was more complex and had greater uncertainty than the prediction of crop yield (RRMSE of 44.5% before and 36.6% after calibration). For long-term site mean EONR predictions, both calibrated and uncalibrated versions can be used as the 16-year mean differences in EONR's were within the historical N rate error range (40-50 kg N ha-1). However, for accurate year-by-year simulation of EONR the calibrated version should be used. Model analysis revealed that higher EONR values in years with above normal spring precipitation were caused by an exponential increase in N loss (denitrification and leaching) with precipitation. We concluded that long-term experimental data were valuable in testing and refining APSIM predictions. The model can be used as a tool to assist N management guidelines in the US Midwest and we identified five avenues on how the model can add value toward agronomic, economic, and environmental sustainability.
Project description:The one-time application of slow-release nitrogen fertilizer can not only reduce the labor input, but also reduce the mechanical input cost, and has the characteristics of slow release and reduce volatilization loss. This research is grounded in a localization trial initiated in 2018, which underwent comprehensive analysis utilizing high-throughput sequencing technology to elucidate the mutual feeding mechanism of slow-release nitrogen fertilizer application rate on microbial community structure, network complexity, and maize yield in different root niches (bulk soil, rhizosphere, and endosphere). Soil characteristics, microbial community composition, and collinear network of different ecological niches under slow-release nitrogen fertilizer were analyzed, and the key core species affecting the stability of the microbial network and the factors driving yield were identified. The results showed that nitrogen application increased the diversity of bacteria, and nitrogen application significantly increased the diversity of rhizosphere bacteria and fungi due to rhizosphere effects. Slow-release nitrogen fertilizer increased the complexity of the bacterial network and decreased the complexity of the fungal network, particularly, the network complexity of bacteria and fungi in the rhizosphere was higher than that in the bulk soil and the rhizosphere. The application of slow-release nitrogen fertilizer increased the abundance of Proteobacteria, Bacteroidota, Gemmatimonadota, Actinobacteria, Ascomycota, Basidiomycota and other dominant bacteria. Coordinate soil physical and chemical properties, increase soil enzyme activity and soil nutrients, improve soil microenvironment, regulate microbial community composition, and promote above-ground yield increase, in which nitrogen application, urease, nitrate reductase and nitrate nitrogen are the main driving factors for yield increase. These findings provide a new idea for the mutual feeding mechanism of slow-release nitrogen fertilizer on microbial diversity and yield in different ecological niches. To selection of suitable nitrogen application rate and regional ecological security in the agro-pastoral ecotone.It offers a theoretical framework for establishing optimal nitrogen application rates and ensuring food security in agro-pastoral ecotones.
Project description:Availability of nitrogen (N) in soil changes the composition and activities of microbial community, which is critical for the processing of soil organic matter and health of crop plants. Inappropriate application of N fertilizer can alter the rhizosphere microbial community and disturb the soil N homeostasis. The goal of this study was to assess the effect of different ratio of N fertilizer at various early to late growth stages of rice, while keeping the total N supply constant on rice growth performance, microbial community structure, and soil protein expression in rice rhizosphere. Two different N regimes were applied, i.e., traditional N application (NT) consists of three sessions including 60, 30 and 10% at pre-transplanting, tillering and panicle initiation stages, respectively, while efficient N application (NF) comprises of four sessions, i.e., 30, 30, 30, and 10%), where the fourth session was extended to anthesis stage. Soil metaproteomics combined with Terminal Restriction Fragment Length Polymorphism (T-RFLP) were used to determine the rhizosphere biological process. Under NF application, soil enzymes, nitrogen utilization efficiency and rice yield were significantly higher compared to NT application. T-RFLP and qPCR analysis revealed differences in rice rhizosphere bacterial diversity and structure. NF significantly decreased the specific microbes related to denitrification, but opposite result was observed for bacteria associated with nitrification. Furthermore, soil metaproteomics analysis showed that 88.28% of the soil proteins were derived from microbes, 5.74% from plants, and 6.25% from fauna. Specifically, most of the identified microbial proteins were involved in carbohydrate, amino acid and protein metabolisms. Our experiments revealed that NF positively regulates the functioning of the rhizosphere ecosystem and further enabled us to put new insight into microbial communities and soil protein expression in rice rhizosphere.
Project description:Corn-soybean rotation is a cropping pattern to optimize crop structure and improve resource use efficiency, and nitrogen (N) fertilizer application is an indispensable tool to increase corn yields. However, the effects of N fertilizer application levels on corn yield and soil N storage under corn-soybean rotation have not been systematically studied. The experimental located in the central part of the Songnen Plain, a split-zone experimental design was used with two planting patterns of continuous corn (CC) and corn-soybean rotations (RC) in the main zone and three N application rates of 0, 180, and 360 kg hm-2 of urea in the secondary zone. The research has shown that RC treatments can enhance plant growth and increase corn yield by 4.76% to 79.92% compared to CC treatments. The amount of N fertilizer applied has a negative correlation with yield increase range, and N application above 180 kg hm-2 has a significantly lower effect on corn yield increase. Therefore, a reduction in N fertilizer application may be appropriate. RC increased soil N storage by improving soil N-transforming enzyme activity, improving soil N content and the proportion of soil organic N fractions. Additionally, it can improve plant N use efficiency by 1.4%-5.6%. Soybeans grown in corn-soybean rotations systems have the potential to replace more than 180 kg hm-2 of urea application. Corn-soybean rotation with low N inputs is an efficient and sustainable agricultural strategy.
Project description:Organic manure has been proposed to substitute part of the chemical fertilizers. However, past research was usually conducted in regimes with excessive nitrogen (N) fertilization, which was not conducive to the current national goal of green and sustainable development. Therefore, exploring the potential of organic fertilizer substitution for mineral N fertilizer under regimes with reduced N inputs is important to further utilize organic fertilizer resources and establish sustainable nutrient management recommendations in the winter wheat (Triticum aestivum L.) - summer maize (Zea mays L.) rotation system in North-central China. In this study, a 4-year field experiment was conducted to investigate the effects of different chicken manure substitution ratios on crop yield, N recovery efficiency (REN), soil N and soil organic matter contents, to clarify the optimal organic substitution ratio of N fertilizer under reduced N application (from 540 kg N ha-1 year-1 to 400 kg N ha-1 year-1). Six substitution ratios were assessed: 0%, 20%, 40%, 60%, 80% and 100% under 200 kg N ha-1 per crop season, respectively, plus a control with no N application from chemical fertilizer or chicken manure. Results showed that the highest yield was achieved under the 20% substitution ratio treatment, with 1.1% and 2.3% higher yield than chemical N alone in wheat season and maize seasons, respectively. At the chicken manure substitution ratios of 20% in wheat season and 20%-40% in maize season, the highest REN reached to 31.2% and 26.1%, respectively. Chicken manure application reduced soil residual inorganic N with increasing substitution ratio. All organic substitution treatments increased soil organic matter and total N content. Implementing 20% organic substitution in wheat season and 20%-40% in maize season under the reduced N application regime in the North-central China is therefore recommended in order to achieve high crop yields and REN, improve soil fertility and enhance livestock manure resource utilization.
Project description:This study investigates whether coupling crop modeling and machine learning (ML) improves corn yield predictions in the US Corn Belt. The main objectives are to explore whether a hybrid approach (crop modeling + ML) would result in better predictions, investigate which combinations of hybrid models provide the most accurate predictions, and determine the features from the crop modeling that are most effective to be integrated with ML for corn yield prediction. Five ML models (linear regression, LASSO, LightGBM, random forest, and XGBoost) and six ensemble models have been designed to address the research question. The results suggest that adding simulation crop model variables (APSIM) as input features to ML models can decrease yield prediction root mean squared error (RMSE) from 7 to 20%. Furthermore, we investigated partial inclusion of APSIM features in the ML prediction models and we found soil moisture related APSIM variables are most influential on the ML predictions followed by crop-related and phenology-related variables. Finally, based on feature importance measure, it has been observed that simulated APSIM average drought stress and average water table depth during the growing season are the most important APSIM inputs to ML. This result indicates that weather information alone is not sufficient and ML models need more hydrological inputs to make improved yield predictions.
Project description:The excessive use of synthetic nitrogen (N) fertilizers in rice (Oryza sativa L.) has resulted in high N loss, soil degradation, and environmental pollution in a changing climate. Soil biochar amendment is proposed as a climate change mitigation tool that supports carbon sequestration and reduces N losses and greenhouse gas (GHG) emissions from the soil. The current study evaluated the impact of four different rates of biochar (B) (C/B0-0 t ha-1, B1-20 t ha-1, B2-40 t ha-1, and B3-60 t ha-1) and two N levels (N1; low (270 kg N ha-1) and N2; high (360 kg N ha-1)), on rice (cultivar Zhenguiai) grown in pots. Significant increases in the average soil microbial biomass N (SMBN) (88%) and carbon (87%) were recorded at the highest rate of 60-ton ha-1B and 360 kg N ha-1 compared to the control (N1C) during both seasons (S1 and S2). The photochemical efficiency (Fv/Fm), quantum yield of the photosystem (PS) II (ΦPS II), electron transport rate (ETR), and photochemical quenching (qP) were enhanced at low rates of biochar applications (20 to 40 t B ha-1) for high and low N rates across the seasons. Nitrate reductase (NR), glutamine synthetase (GS), and glutamine 2-oxoglutarate aminotransferase (GOGAT) activity were, on average, 39%, 55%, and 63% higher in the N1B3, N2B2, and N2B3 treatments, respectively than the N1C. The grain quality was higher in the N1B3 treatment than the N1C, i.e., the protein content (PC), amylose content (AC), percent brown rice (BRP), and percent milled rice (MRP) were, on average, 16%, 28%, 4.6%, and 5% higher, respectively in both seasons. The results of this study indicated that biochar addition to the soil in combination with N fertilizers increased the dry matter (DM) content, N uptake, and grain yield of rice by 24%, 27%, and 64%, respectively, compared to the N1C.
Project description:This study was designed to investigate the effects of Azospirillum brasilense and Bradyrhizobium sp. co-inoculation coupled with N application on soil N levels and N in plants (total N, nitrate N-NO3− and ammonium N-NH4+), photosynthetic pigments, cowpea plant biomass and grain yield. An isotopic technique was employed to evaluate 15N fertilizer recovery and derivation. Field trials involved two inoculations—(i) single Bradyrhizobium sp. and (ii) Bradyrhizobium sp. + A. brasilense co-inoculation—and four N fertilizer rates (0, 20, 40 and 80 kg ha−1). The co-inoculation of Bradyrhizobium sp. + A. brasilense increased cowpea N uptake (an increase from 10 to 14%) and grain yield (an average increase of 8%) compared to the standard inoculation with Bradyrhizobium sp. specifically derived from soil and other sources without affecting 15N fertilizer recovery. There is no need for the supplementation of N via mineral fertilizers when A. brasilense co-inoculation is performed in a cowpea crop. However, even in the case of an NPK basal fertilization, applied N rates should remain below 20 kg N ha−1 when co-inoculation with Bradyrhizobium sp. and A. brasilense is performed.
Project description:The integrated application of chemical and organic fertilizers has been demonstrated to enhance soil fertility and the sustainable production of cotton yields. However, the impact of different fertilizer formulations on the sustainability of cotton production and soil quality over time have not been widely discussed. Here, we aimed to systematically evaluate the impact of different fertilization regimes [no fertilizer(CK), single application of chemical fertilizer(CF), 75% chemical fertilizer + 25% organic fertilizer (M1), 50% chemical fertilizer + 50% organic fertilizer (M2), 25% chemical fertilizer + 75% organic fertilizer (M3)] on soil quality, yield and yield sustainability in cotton fields in 2023 through a 10-year (2014-2023) field trial. Results showed that: (1) Compared to the natural state, different fertilization treatments significantly increased the average annual cotton yield and sustainable yield index (SYI) (P< 0.001), with the M1 treatment having the highest yield and the M2 treatment having the highest sustainable yield index (SYI). (2) Soil organic matter, soil total nitrogen, soil ammonium nitrogen, soil alkaline dissolved nitrogen, soil available phosphorus, and soil available potassium content showed the highest increase under the M1 treatment as compared to the natural state (P< 0.001). (3) Soil alkaline phosphatase enzyme activity was significantly increased by different fertilization treatments compared to the natural state (P< 0.05), M1, M2 and M3 treatments significantly increased soil urease enzyme activity and soil catalase enzyme activity (P< 0.001). (4) The random forest analysis showed that soil organic matter, soil nitrogen fractions (soil total nitrogen, soil ammonium nitrogen, soil alkali-hydrolyzable nitrogen, soil nitrate nitrogen), and available potassium content played a pivotal role in determining the yield and yield sustainability of cotton. (5) The highest soil quality index (SQI) value was observed in the M1. A markedly positive correlation was observed between the SQI and SYI (y = 0.03892x + 0.59609, R2 = 0.90379, P < 0.001), highlighting that the SQI constituted a significant factor in the sustainable production of cotton. These findings suggest that long-term application of chemical and organic fertilizers is an effective strategy for improving soil quality and cotton yield in continuous cropping while also contributing toward a more sustainable agricultural system.