Drip fertigation sustains crop productivity while mitigating reactive nitrogen losses in Chinese agricultural systems: Evidence from a meta-analysis.

Drip fertigation can synchronize the supply of nutrients and water for crop demand, offering the potential for minimizing negative environmental impacts and sustaining crop productivity. However, there are no comprehensive evaluations on performances of drip fertigation on environmental nitrogen (N) losses and crop productivity, nationwide. Here, a meta-analysis was performed to quantify overall effects of drip fertigation on N losses and crop productivity in Chinese agricultural systems based on 443 observations from 42 field studies. The results showed that drip fertigation significantly increased crop yields by 9.8 % and slightly increased soil NO emission by 13.9 % compared to the traditional irrigation and fertilization practices (e.g. flooding/furrow irrigation and N broadcasting), while significantly decreasing NH3 volatilization by 14.2 %, soil N2O emission by 28.1 % and NO3--N leaching loss by 71.2 %. There were significant mitigation potentials of environmental N losses by drip fertigation for cereal cropping systems, not for horticultural crops in terms of soil NO emission and not for cotton in terms of NH3 volatilization. Non significant promotion effect on NO emission and significant reduction effects on the other all kinds of environmental N losses by drip fertigation were observed for alkaline soils (pH > 7.3) and coarse-textured soils. In addition, the use of different fertilizer sources and/or soil amendments have shown in popularity as strategies to offset the negative feedback associated with agricultural N losses, no direct synthetic result was shown in drip-fertigated soils. We synthesized 19 studies so as to assess the potential mitigation options for further minimizing N losses in drip fertigation systems, which suggested that deleterious environmental pollution could be further reduced while still achieving high crop yields with a combination of enhanced-efficiency fertilizers (e.g. nitrification or urease inhibitors) or soil amendments (e.g. biochar or straw) to drip fertigation systems.

Blending urea and slow-release nitrogen fertilizer increases dryland maize yield and nitrogen use efficiency while mitigating ammonia volatilization.

Agricultural non-point source pollution has become the main pollution source in China. Ammonia (NH3) volatilization is one of the main factors of agricultural non-point source pollution. Slow-release nitrogen fertilizer (S) has been widely recognized as an efficient management measure to increase crop yields and mitigate NH3 volatilization. However, few studies have reported the effects of urea (U) blended with slow-release nitrogen fertilizer (UNS) on maize yield and NH3 volatilization under dryland farming conditions. A two-season field experiment with U, S and various blending ratios of U and S (UNS) under two N application rates (N1: 180 kg N ha-1, N2: 240 kg N ha-1) was conducted to determine their effects on maize yield, NH3 volatilization and residual soil NO3--N. The results showed that UNS substantially reduced NH3 volatilization compared with U, primarily because of the relatively low soil pH and electrical conductivity, and the relatively high soil organic matter. UNS significantly increased dry matter, grain yield, N uptake and N use efficiency (NUE), but reduced residual soil NO3--N compared with U and S. Among UNS treatments, the blending ratio of U and S at 3:7 (UNS2) was most effective in improving maize yield and NUE, while mitigating NH3 volatilization and soil NO3--N leaching. N1 not only reduced N losses, but also increased NUE compared with N2. In conclusion, UNS2N1 is recommended as the best N fertilizer application strategy for the sustainable production of dryland maize in northwest China.

Combined application of soluble organic and chemical fertilizers in drip fertigation improves nitrogen use efficiency and enhances tomato yield and quality.

BACKGROUND: Sustainable greenhouse tomato production requires optimal fertilizer management to achieve the double-win strategy of producing high yields and maximizing profits with less environmental pollution. The objective of this study was to seek an optimal fertilization strategy maintaining high productivity of greenhouse tomato, improving nitrogen use efficiency and reducing nitrate leaching risk. RESULTS: The combined application of soluble organic and chemical fertilizers for topdressing (SOSC) not only produced more fruit yield (75.18 Mg ha-1 ) and plant dry matter (10 449.12 kg ha-1 ), but also enhanced plant nutrients uptake, nitrogen recovery efficiency (39.22%), nitrogen agronomic efficiency (176.78 kg kg-1 ), soluble solids, vitamin C and lycopene content in tomato fruits compared with the other treatments, that is chicken manures for basal application and chemical fertilizer for topdressing (CC), soluble organic fertilizer for topdressing (SO) and soluble chemical fertilizer for topdressing (SC). In terms of soil nutrients residue, SOSC had no obvious NO3 - -N accumulation area in the 0-60 cm soil layer, unlike large accumulation in the soil layer below 30 cm in SO and SC. CONCLUSION: The combined application of soluble organic and chemical fertilizers is highly recommended to sustain fruit yield, improve nitrogen use efficiency and reduce soil degradation risks in commercial greenhouse tomato production.

[Effects of the frequency and amount of drip irrigation on yield, tuber quality and water use efficiency of potato in sandy soil of Yulin, northern Shaanxi, China]. / 滴灌频率和灌水量对榆林沙土马铃薯产量、品质和水分利用效率的影响.

Reasonable irrigation is still lacking for potato production in the sandy areas of Yulin, northern Shaanxi Province. To solve this problem, field drip fertigation was conducted to examine the growth, yield and quality of potato during the whole growing season. We further analyzed the responses of these indices to different irrigation frequencies and amounts. There were three irrigation frequencies (d), i.e. 4 (D1), 8 (D2) and 10 (D3), and three irrigation amounts, i.e. 60%ETc (W1), 80%ETc(W2) and 100%ETc(W3), where ETc was the crop water requirement, resulting in a total of nine treatments. Under the same irrigation frequency, plant height, leaf area index, dry matter, tuber yield and economic benefits of W3 were higher than those of W1 and W2. W1 had the highest irrigation water use efficiency (IWUE), while water use efficiency was not significantly affected by irrigation amount. The average tuber yield of W3 was 43442 kg·hm-2, which was 23.3% and 11.6% higher than that of W1 and W2, respectively. The net income of W3 was 23492 yuan·hm-2, which was 40.4% and 18.7% higher than that of W1 and W2, respectively. Tubers from W3 had the highest starch and vitamin C contents but the lowest reducing sugar content, which were 14.4%, 18.54 mg·(100 g)-1 FW and 0.7%, respectively. At the same irrigation amount, tuber yield, IWUE, starch and vitamin C contents of D1 were the highest, but the reducing sugar content was the lowest at the low and medium irrigation amounts. At the high irrigation amount, D2 had the highest tuber yield, IWUE, net income, starch and vitamin C contents but the lowest reducing sugar content, which were 46572 kg·hm-2, 23.04 kg·m-3, 26,622 yuan·hm-2,14.6%, 19.53 mg·(100 g)-1 FW and 0.7%, respectively. Based on the interacting effects of drip irrigation frequency and amount, both yield and quality of D2W3 reached the maximum. Results from the principal component analysis showed that D2W3 had the highest score. D2W3(8 d, 100%ETc) had the greatest yield and quality and relatively higher water use efficiency, which was thus considered as the optimal combination of drip irrigation frequency and amount. The results could provide a scientific basis for the drip irrigation scheduling design for high-yield, high-efficiency and high-quality potato production in the sandy areas of Yulin, northern Shaanxi.

Comparison of neuron-based, kernel-based, tree-based and curve-based machine learning models for predicting daily reference evapotranspiration.

Accurately predicting reference evapotranspiration (ET0) with limited climatic data is crucial for irrigation scheduling design and agricultural water management. This study evaluated eight machine learning models in four categories, i.e. neuron-based (MLP, GRNN and ANFIS), kernel-based (SVM, KNEA), tree-based (M5Tree, XGBoost) and curve-based (MARS) models, for predicting daily ET0 with maximum/maximum temperature and precipitation data during 2001-2015 from 14 stations in various climatic regions of China, i.e., arid desert of northwest China (NWC), semi-arid steppe of Inner Mongolia (IM), Qinghai-Tibetan Plateau (QTP), (semi-)humid cold-temperate northeast China (NEC), semi-humid warm-temperate north China (NC), humid subtropical central China (CC) and humid tropical south China (SC). The results showed machine learning models using only temperature data obtained satisfactory daily ET0 estimates (on average R2 = 0.829, RMSE = 0.718 mm day-1, NRMSE = 0.250 and MAE = 0.508 mm day-1). The prediction accuracy was improved by 7.6% across China when information of precipitation was further considered, particularly in (sub)tropical humid regions (by 9.7% in CC and 12.4% in SC). The kernel-based SVM, KNEA and curve-based MARS models generally outperformed the others in terms of prediction accuracy, with the best performance by KNEA in NWC and IM, by SVM in QTP, CC and SC, and very similar performance by them in NEC and NC. SVM (1.9%), MLP (2.0%), MARS (2.6%) and KNEA (6.4%) showed relatively small average increases in RMSE during testing compared with training RMSE. SVM is highly recommended for predicting daily ET0 across China in light of best accuracy and stability, while KNEA and MARS are also promising powerful models.

Throughfall and stemflow heterogeneity under the maize canopy and its effect on soil water distribution at the row scale.

Quantitative knowledge of the spatial variability of soil infiltration processes caused by canopy rainfall redistribution has significant hydro-chemical consequences owing to their influence on nutrients leaching and groundwater recharge in agricultural ecosystems. The heterogeneity of throughfall and stemflow under the maize canopy was quantified in this study, and its subsequent effect on soil water distribution at the row scale was further examined. Throughfall at 15 locations within and between maize rows as well as stemflow was observed over three growing seasons of 2015, 2016 and 2017 on the Loess Plateau of China. Soil water content at five depths in the row and interrow positions were continuously monitored. The results showed that throughfall was significantly different among the five sampling sections between maize rows, with the highest throughfall in the center and a decreasing trend towards the maize row. Greater throughfall was observed on the windward side of the maize row than on the leeward side. These spatial patterns persisted for most rainfall events. However, much higher net rainfall (throughfall plus stemflow) was obtained in the row positions when stemflow was further considered. Net rainfall reaching the row positions resulted not only in earlier water infiltration, but also in deeper penetration. The results suggested that the presence of maize canopy altered the soil surface water flux and thus caused heterogeneous infiltration water in the soil, which has implications for guiding the placing of fertilizers/pesticides and soil erosion management in the maize field.

Using electrical resistivity tomography to differentiate sapwood from heartwood: application to conifers.

Estimating sapwood area is one of the main sources of error when upscaling point scale sap flow measurements to whole-tree water use. In this study, the potential use of electrical resistivity tomography (ERT) to determine the sapwood-heartwood (SW-HW) boundary is investigated for Pinus elliottii Engelm var. elliottii × Pinus caribaea Morelet var. hondurensis growing in a subtropical climate. Specifically, this study investigates: (i) how electrical resistivity is correlated to either wood moisture content, or electrolyte concentration, or both, and (ii) how the SW-HW boundary is defined in terms of electrical resistivity. Tree cross-sections at breast height are analysed using ERT before being felled and the cross-section surface sampled for analysis of major electrolyte concentrations, wood moisture content and density. Electrical resistivity tomography results show patterns with high resistivities occurring in the inner part of the cross-section, with much lower values towards the outside. The high-resistivity areas were generally smaller than the low-resistivity areas. A comparison between ERT and actual SW area measured after felling shows a slope of the linear regression close to unity (=0.96) with a large spread of values (R(2) = 0.56) mostly due to uncertainties in ERT. Electrolyte concentrations along sampled radial transects (cardinal directions) generally showed no trend from the centre of the tree to the bark. Wood moisture content and density show comparable trends that could explain the resistivity patterns. While this study indicates the potential for application of ERT for estimating SW area, it shows that there remains a need for refinement in locating the SW-HW boundary (e.g., by improvement of the inversion method, or perhaps electrode density) in order to increase the robustness of the method.