European Journal of Agronomy最新文献_第2页

Yield more in the shadow: Mitigating shading-induced yield penalty of maize via optimizing source-sink carbon partitioning 在阴影中增产：通过优化源-汇碳分配减轻玉米因阴影引起的产量损失

IF 4.5 1区农林科学 Q1 AGRONOMY

European Journal of Agronomy

Pub Date : 2024-11-08 DOI: 10.1016/j.eja.2024.127421

Xiao-Gui Liang , Hui-Min Chen , Yu-Qiang Pan , Zhi-Wei Wang , Cheng Huang , Zhen-Yuan Chen , Wang Tang , Xian-Min Chen , Si Shen , Shun-Li Zhou

Global solar radiation has been decreasing, posing a great threat to food security by reducing photo-assimilation and disrupting carbon (C) partitioning in crops like maize. However, practical countermeasures to cope with source-sink balance in periodic shading stress are lacking. Here, we first simulated shading stresses with different degrees and occurring periods on field maize for two years. Results verified that shading-induced yield penalties are most severe around silking and are closely associated with biomass allocation, implying a significant imbalance of source: sink C partitioning during silking. To mitigate yield losses from shading, detasseling (Det) and synchronous pollination (SP), targeting the two sink tissues (tassel and ear, respectively), were applied to 70 % shading at the silking stage in two seasons. Both practices conferred benefits to grain number and yield production, with final yield increases ranging from 4.0 % to 31.3 % under shading. Through ¹³C labeling, sugar metabolism assay and global analysis, we proved that Det improved the source-sink balance via increasing light irradiance within the canopy and eliminating apical dominance to stimulate C assimilates partitioning into the ear. SP promoted C partitioning into the ear by increasing reproductive sink strength and optimizing assimilates allocation among grain siblings. Intriguingly, Det and SP also provided marginal yield increase under normal light conditions. Our findings underscore the potential of source-sink coordination and C partitioning in mitigating maize yield penalty under environmental stresses like shading. The research also provides new avenues for developing agronomic practices and breeding strategies via tasseling and silking regulation, aiming to improve maize crop production and stress resilience and ensure food security in the face of climate change.

全球太阳辐射不断减少，降低了光同化作用，破坏了玉米等作物的碳（C）分配，从而对粮食安全构成了巨大威胁。然而，目前还缺乏应对周期性遮光胁迫下源汇平衡的实用对策。在此，我们首先模拟了不同程度和不同时期的遮光胁迫对大田玉米造成的影响。结果证实，遮光导致的产量损失在吐丝前后最为严重，并且与生物量分配密切相关，这意味着在吐丝期间源：汇碳分配严重失衡。为了减轻遮光造成的产量损失，在两季中，分别针对两个吸收汇组织（穗和穗）采用了脱穗（Det）和同步授粉（SP）措施，在吐丝期遮光率达到 70%。两种方法都对谷粒数量和产量产生了益处，遮光下的最终产量增加了 4.0% 至 31.3%。通过 13C 标记、糖代谢测定和全局分析，我们证明了 Det 可通过增加冠层内的光辐照度和消除顶端优势来改善源汇平衡，从而促进 C 同化物分配到果穗中。SP 则通过增加生殖汇强度和优化同化物在籽粒兄弟姐妹间的分配来促进穗内的碳分配。有趣的是，在正常光照条件下，Det 和 SP 也能带来边际增产。我们的研究结果强调了源汇协调和碳分配在减轻玉米在遮光等环境胁迫下的产量损失方面的潜力。这项研究还为通过抽穗和吐丝调控制定农艺实践和育种策略提供了新途径，旨在提高玉米作物产量和抗逆性，确保气候变化下的粮食安全。

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引用次数: 0

Using UAV-based multispectral and RGB imagery to monitor above-ground biomass of oat-based diversified cropping 利用基于无人机的多光谱和 RGB 图像监测以燕麦为基础的多样化种植的地上生物量

IF 4.5 1区农林科学 Q1 AGRONOMY

European Journal of Agronomy

Pub Date : 2024-11-08 DOI: 10.1016/j.eja.2024.127422

Pengpeng Zhang , Bing Lu , Junyong Ge , Xingyu Wang , Yadong Yang , Jiali Shang , Zhu La , Huadong Zang , Zhaohai Zeng

Timely access to crop above-ground biomass (AGB) information is crucial for estimating crop yields and managing water and fertilizer efficiently. Unmanned aerial vehicle (UAV) imagery offers promising avenues for AGB estimation due to its high efficiency and flexibility. However, the accuracy of these estimations can be influenced by various factors, including crop growth stages, the spectral resolution of UAV sensors, and flight altitudes. These factors need thorough investigation, especially in diversified cropping systems where crop diversity and growth stages interplay complexly, challenging the accuracy of AGB estimation. This study aims to estimate AGB of oats planted under different agricultural regimes—monoculture, crop rotation, and intercropping—at various growth stages (jointing, flowering, and grain-filling) and across all stages combined, using multispectral and RGB UAV images collected at different flight altitudes (25 m, 50 m, and 100 m). Three feature selection methods—maximal information coefficient (MIC), least absolute shrinkage and selection operator (LAS), and recursive feature elimination (RFE)—were tested. Four machine learning models—ridge regression (RR), multilayer perceptron (MLP), light gradient boosting machine (LGBM), and extreme gradient boosting (XGBoost)—were used for estimating AGB. Each feature selection method was combined with each machine learning model (e.g., MIC-RR, MIC-MLP, MIC-LGBM, MIC-XGBoost, LAS-RR) to evaluate their performance. Results revealed that the highest accuracy in AGB estimation was achieved with images acquired at a flight altitude of 25 m. The RFE-MLP model demonstrated superior results during the jointing stage (R² = 0.84, root mean squared error (RMSE) = 217.45 kg/ha, root mean squared logarithmic error (RMSLE) = 0.16, mean absolute percentage error (MAPE) = 4.15 %), the LAS-RR model excelled in the flowering stage (R² = 0.85, RMSE = 263.03 kg/ha, RMSLE = 0.05, MAPE = 14.44 %), and the RFE-XGBoost model was most effective during the grain-filling stage (R² = 0.68, RMSE = 865.03 kg/ha, RMSLE = 0.12, MAPE = 8.88 %). For cross-stage modelling, the RFE-MLP achieved remarkable results (R² = 0.93, RMSE = 680.44 kg/ha, RMSLE = 0.16, MAPE = 12.12 %). This study demonstrates the efficacy of combining feature selection methods with machine learning algorithms to enhance the accuracy of oat AGB estimations. The involvement of multiple cropping patterns enhances the generalizability of our findings, facilitating real-time and rapid monitoring of crop growth in future diversified cropping systems.

及时获取作物地上生物量（AGB）信息对于估算作物产量、有效管理水肥至关重要。无人飞行器（UAV）图像因其高效性和灵活性，为估算作物地上生物量提供了广阔的前景。然而，这些估算的准确性会受到各种因素的影响，包括作物生长阶段、无人飞行器传感器的光谱分辨率和飞行高度。需要对这些因素进行深入研究，尤其是在作物多样性和生长阶段复杂交织的多样化种植系统中，这对 AGB 估测的准确性提出了挑战。本研究旨在利用在不同飞行高度（25 米、50 米和 100 米）采集的多光谱和 RGB 无人机图像，估算在不同农业制度（单作、轮作和间作）下种植的燕麦在不同生长阶段（拔节期、开花期和籽粒饱满期）以及所有阶段的 AGB 总量。测试了三种特征选择方法--最大信息系数 (MIC)、最小绝对收缩和选择算子 (LAS) 以及递归特征消除 (RFE)。四种机器学习模型--脊回归（RR）、多层感知器（MLP）、轻梯度提升机（LGBM）和极端梯度提升（XGBoost）--被用于估算 AGB。每种特征选择方法都与每种机器学习模型（如 MIC-RR、MIC-MLP、MIC-LGBM、MIC-XGBoost、LAS-RR）相结合，以评估它们的性能。结果显示，在飞行高度为 25 米时获取的图像的 AGB 估计精度最高。在接合阶段，RFE-MLP 模型显示出更优越的结果（R² = 0.84，均方根误差 (RMSE) = 217.45 千克/公顷，均方根对数误差 (RMSLE) = 0.16，平均绝对百分比误差 (MAPE) = 4.15 %），LAS-RR 模型在开花期表现出色（R² = 0.85，RMSE = 263.03 kg/ha，RMSLE = 0.05，MAPE = 14.44 %），RFE-XGBoost 模型在谷粒充实期最为有效（R² = 0.68，RMSE = 865.03 kg/ha，RMSLE = 0.12，MAPE = 8.88 %）。在跨阶段建模方面，RFE-MLP 取得了显著效果（R² = 0.93，RMSE = 680.44 kg/ha，RMSLE = 0.16，MAPE = 12.12 %）。这项研究表明，将特征选择方法与机器学习算法相结合可有效提高燕麦 AGB 估测的准确性。多种种植模式的参与增强了我们研究结果的可推广性，有助于在未来的多样化种植系统中实时、快速地监测作物生长情况。

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引用次数: 0

Demonstrating almost half of cotton fiber quality variation is attributed to climate change using a hybrid machine learning-enabled approach 利用混合机器学习方法证明近一半的棉花纤维质量变化归因于气候变化

IF 4.5 1区农林科学 Q1 AGRONOMY

European Journal of Agronomy

Pub Date : 2024-11-07 DOI: 10.1016/j.eja.2024.127426

Xin Li , Zhenggui Zhang , Zhanlei Pan , Guilan Sun , Pengcheng Li , Jing Chen , Lizhi Wang , Kunfeng Wang , Ao Li , Junhong Li , Yaopeng Zhang , Menghua Zhai , Wenqi Zhao , Jian Wang , Zhanbiao Wang

Understanding the effects of climate change on cotton fiber quality will reduce the risks to production caused by global warming. Machine learning algorithms are effective for forecasting climate impacts on crops. However, the impact of climate change on cotton fiber quality is unclear. Hence, a hybrid machine learning-enabled approach, the Bayesian model average (BMA) method with multiple machine learning algorithms (linear regressor, SVR, RFR, GBDT, LightGBM, and XGBoost) and bootstrap resampling, was developed to explore the impact and screen the important climatic factors affecting various traits of fiber quality. On the basis of fiber quality data from 1033 test stations across Xinjiang, China, from 2016 to 2022, the explained variance for climate change in the hybrid machine learning model was as follows: 44.72 %–50.55 % for white cotton grade, 44.06 %–53.95 % for length, 51.72 %–56.81 % for micronaire, 32.70 %–49.50 % for length uniformity, and 45.66 %–53.09 % for strength in the 1000 bootstrapping samples. In addition, recursive feature elimination with cross-validation (RFECV) was used to select the optimal feature set and calculate the contribution of each feature. The variability in micronaire in the hybrid model was affected primarily by climate factors, such as the daily minimum temperature, rainfall, and wind speed, whereas the other quality traits were affected mainly by radiation-related climatic indicators. The climate during the harvest stage in October had a significant effect on cotton quality, explaining 33.0 % of the variance in white cotton grade, 32.1 % in length, and 48.3 % in fiber strength. Conversely, the climate during the boll opening and early harvest stages in September had a greater influence on micronaire and length uniformity, accounting for 21.4 % and 37.2 % of the variance, respectively. This study highlights that climate change explains nearly 50 % of the variation in fiber quality, with the influence being notably more considerable during the later stages of the cotton growth period. These findings clarify the uncertainty in the impact of climate change on cotton fiber quality considering the uncertainty of the single machine model and model errors. Equally important, this information can be valuable for farmers and growers seeking to improve fiber quality under climate change.

了解气候变化对棉花纤维质量的影响将降低全球变暖对生产造成的风险。机器学习算法可有效预测气候对农作物的影响。然而，气候变化对棉花纤维质量的影响尚不明确。因此，我们开发了一种混合机器学习方法，即贝叶斯模型平均法（BMA），该方法采用多种机器学习算法（线性回归器、SVR、RFR、GBDT、LightGBM 和 XGBoost）和引导重采样，以探索影响并筛选出影响纤维质量各种性状的重要气候因素。基于 2016 年至 2022 年中国新疆 1033 个试验站的纤维质量数据，混合机器学习模型对气候变化的解释方差如下：在 1000 个引导样本中，白棉等级的解释方差为 44.72 %-50.55 %，长度的解释方差为 44.06 %-53.95 %，马克隆值的解释方差为 51.72 %-56.81 %，长度均匀性的解释方差为 32.70 %-49.50 %，强度的解释方差为 45.66 %-53.09 %。此外，还使用了交叉验证递归特征消除法（RFECV）来选择最佳特征集，并计算每个特征的贡献率。在混合模型中，马克隆值的变化主要受气候因素的影响，如日最低气温、降雨量和风速，而其他质量性状主要受辐射相关气候指标的影响。10 月份收获期的气候对棉花质量有显著影响，解释了 33.0 % 的白棉等级变异、32.1 % 的长度变异和 48.3 % 的纤维强度变异。相反，9 月棉铃开放和早期收获阶段的气候对棉花的细度和长度均匀性影响更大，分别占变异的 21.4% 和 37.2%。这项研究强调，气候变化可解释近 50% 的纤维质量变异，在棉花生长后期的影响尤其显著。考虑到单机模型的不确定性和模型误差，这些发现澄清了气候变化对棉花纤维质量影响的不确定性。同样重要的是，这些信息对于农民和种植者在气候变化条件下提高纤维质量具有重要价值。

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引用次数: 0

Citrus pose estimation under complex orchard environment for robotic harvesting 复杂果园环境下的柑橘姿态估计，用于机器人收割

IF 4.5 1区农林科学 Q1 AGRONOMY

European Journal of Agronomy

Pub Date : 2024-11-04 DOI: 10.1016/j.eja.2024.127418

Guanming Zhang , Li Li , Yunfeng Zhang , Jiyuan Liang , Changpin Chun

The growth poses of citrus on trees are diverse. To ensure minimal loss during citrus harvesting, accurately estimating the pose of citrus is particularly important. To solve this problem, this research developed a real-time citrus pose estimation system based on neural networks and point cloud processing algorithms. Specifically, this method uses neural networks to identify citrus. After constructing the citrus point cloud, it is input into the Random Sample Consensus with Levenberg-Marquardt (RANSAC-LM) point cloud processing algorithm to obtain the citrus coordinates. Combined with citrus growth information, the pose is output. By analyzing the distribution of citrus poses, citrus poses convenient for end- effector harvesting are defined. To enhance the camera's ability to obtain information about citrus, a camera observation model is constructed to dynamically adjust the camera position. Through experiments, the appropriate deep learning target detection framework YOLO V5 is selected for citrus object detection. The precision (P), recall rate (R), and mean average precision (mAP) are 92.3 %, 79.1 %, and 88.5 % respectively. This network can handle detection tasks in real orchard environments. The original Random Sample Consensus (RANSAC) is improved by using the Levenberg-Marquardt (LM) nonlinear optimization method. Experimental results show that RANSAC-LM reduces the citrus center coordinate precision error from (0.2, 0.2, 2.3) mm to (0.1, 0.2, 1.4) mm, reduces the accuracy Spherical Error Probable (SEP) from 2.77 to 1.61, and finally reduces the citrus pose error from 5.72° to 2.43°. The efficiency of the proposed citrus pose estimation algorithm is 0.24 s. Deployed on a citrus picking robot, it verifies the feasibility of the algorithm and provides a new solution for the pose estimation problem of citrus harvesting robots.

柑橘在树上的生长姿态多种多样。为了确保在柑橘采收过程中损失最小，准确估计柑橘的姿态尤为重要。为解决这一问题，本研究开发了一种基于神经网络和点云处理算法的实时柑橘姿态估计系统。具体来说，该方法使用神经网络来识别柑橘。在构建柑橘点云后，将其输入随机样本共识与莱文伯格-马夸特（RANSAC-LM）点云处理算法，以获得柑橘坐标。结合柑橘生长信息，输出柑橘姿态。通过分析柑橘姿态的分布，确定了便于末端效应器收割的柑橘姿态。为了提高摄像头获取柑橘信息的能力，还构建了一个摄像头观测模型来动态调整摄像头位置。通过实验，为柑橘目标检测选择了合适的深度学习目标检测框架 YOLO V5。其精度（P）、召回率（R）和平均精度（mAP）分别为 92.3%、79.1% 和 88.5%。该网络可以处理真实果园环境中的检测任务。利用 Levenberg-Marquardt (LM) 非线性优化方法对原始随机抽样共识（RANSAC）进行了改进。实验结果表明，RANSAC-LM 将柑橘中心坐标精度误差从 (0.2, 0.2, 2.3) mm 降低到 (0.1, 0.2, 1.4) mm，将精度球形误差概率 (SEP) 从 2.77 降低到 1.61，并最终将柑橘姿态误差从 5.72° 降低到 2.43°。所提出的柑橘姿态估计算法的效率为 0.24 秒。在柑橘采摘机器人上的应用验证了该算法的可行性，并为柑橘采摘机器人的姿态估计问题提供了一种新的解决方案。

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引用次数: 0

Shallow drains and straw mulch alleviate multiple constraints to increase sunflower yield on a clay-textured saline soil I. Effects of decreased soil salinity, waterlogging and end-of-season drought 浅层排水沟和秸秆覆盖减轻多种制约因素，提高粘质盐碱土上的向日葵产量 I. 土壤盐分降低、涝害和季末干旱的影响

IF 4.5 1区农林科学 Q1 AGRONOMY

European Journal of Agronomy

Pub Date : 2024-11-04 DOI: 10.1016/j.eja.2024.127416

Mohammad Nazrul Islam , Richard W. Bell , Edward G. Barrett-Lennard , Mohammad Maniruzzaman

A well-designed drainage system can alleviate soil salinity and waterlogging, leading to increased crop yield if the drainage does not cause a water shortage late in the growing season. We conducted three field experiments with sunflower across two dry seasons (Experiment I in 2019–20, and II and III in 2020–21) in a tropical landscape to examine the effectiveness of shallow drains and mulch in overcoming these constraints. In Experiment I, four surface drains of 0.1 or 0.2 m depth spaced 1.2 or 1.8 m apart were tested along with an undrained treatment. In Experiment II, the same four drainage treatments and an undrained treatment in the main plots were split into mulch (-M and +M) sub-plots. Experiment III had four main treatments, undrained, surface drains (SD; 0.1 m deep, 1.8 m apart), subsoil drains (SSD; 0.5 m deep, 4.5 m apart) and SSD+SD each split for mulch (-M and +M) sub-plots. At vegetative emergence and at the 8-leaf stage, all plots were inundated (3–5 cm depth; EC_w: 1.5–2.5 dS m^–1) for 24 h before opening the drains. Drainage treatments without mulch reduced SEW₃₀ (waterlogging index, sum of excess water within 30 cm soil depth) and soil EC_1:5 at 0–15 cm, while increasing sunflower yield by 15–100 % compared to the undrained no-mulch treatment. Relative to the undrained no-mulch treatment, drains with straw mulch conserved soil water, reduced EC_1:5 at 0–15 cm and increased yield in Experiments II and III by 40–47 and 76–143 %, respectively. There were no yield differences among the combinations of shallow drains. Although combined drains (SSD+SD) added 25–30 % extra yield relative to surface drains, these have higher installation costs. Shallow surface drains at 1.2–1.8 m spacing coupled with mulch are effective options for smallholder farmers to reduce salinity, waterlogging and drought stresses, and increase yield on saline, clay soils.

精心设计的排水系统可以缓解土壤盐碱化和涝害，如果排水系统不会在生长季节后期造成缺水，则可提高作物产量。我们在热带地区的两个旱季对向日葵进行了三次田间试验（试验 I 于 2019-20 年进行，试验 II 和 III 于 2020-21 年进行），以考察浅层排水沟和地膜在克服这些限制因素方面的效果。在实验 I 中，测试了四个深度为 0.1 米或 0.2 米、间距为 1.2 米或 1.8 米的地表排水沟以及一个不排水处理。在实验二中，主地块中的四个排水处理和一个不排水处理被分成地膜覆盖（-M 和 +M）子地块。实验三有四个主要处理，分别是未排水处理、地表排水处理（SD；深 0.1 米，间距 1.8 米）、底土排水处理（SSD；深 0.5 米，间距 4.5 米）和 SSD+SD 处理，每个处理都分成地膜覆盖（-M 和 +M）子地块。在植株萌发和 8 叶期，所有地块都被淹没（3-5 厘米深；ECw：1.5-2.5 dS m-1）24 小时，然后再打开排水沟。与未排水、未覆盖地膜的处理相比，未覆盖地膜的排水处理降低了 SEW30（涝害指数，30 厘米土壤深度内多余水分的总和）和 0-15 厘米处的土壤 EC1:5，同时使向日葵产量提高了 15-100%。与未排水的无覆盖物处理相比，在试验 II 和 III 中，排水沟加稻草覆盖物可保持土壤水分，降低 0-15 厘米处的 EC1:5，并使产量分别增加 40-47 % 和 76-143 %。浅层排水沟组合之间没有产量差异。虽然组合排水沟（SSD+SD）比地表排水沟增产 25-30%，但其安装成本较高。间距为 1.2-1.8 米的浅层地表排水沟与覆盖物相结合，是小农减少盐碱、涝害和干旱压力并提高盐碱粘土产量的有效选择。

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引用次数: 0

Estimating the temperature sensitivity of rice (Oryza sativa L.) yield and its components in China using the CERES-Rice model 利用 CERES-Rice 模型估算中国水稻（Oryza sativa L. ）产量及其组成部分的温度敏感性

IF 4.5 1区农林科学 Q1 AGRONOMY

European Journal of Agronomy

Pub Date : 2024-11-04 DOI: 10.1016/j.eja.2024.127419

Zeyu Zhou , Jiming Jin , Fei Li , Jian Liu

The effects of temperature changes on rice (Oryza sativa L.) yield and its components have been widely documented. However, most existing studies are based on small-scale, short-term field experiments, with few assessing these effects on a large scale or over long periods. Here, the calibrated Crop Environment Resource Synthesis (CERES)-Rice model was used for numerical simulations over six climate regions in the major rice cultivation areas of China for the period of 1989–2018. The simulated results were used to estimate the temperature sensitivity of rice yield with a panel model in each climate region, and the yield sensitivity was then decomposed into the temperature sensitivity of three components: panicle number per unit area (Pan_no), filled grain number per panicle (Grain_no), and grain weight (Grainwt). Results indicated that rice yield exhibited negative temperature sensitivity across all climate regions, driven primarily by the temperature sensitivity of Grain_no in most regions. Additionally, Grainwt did not vary with temperature in all regions. Further analysis suggested that yield, Pan_no, and Grain_no were more sensitive to high temperature degree days (HDD) than to growing degree days (GDD). Under the warmer scenarios, HDD increase played a dominant role in the reduction of Grain_no, while the joint effect of GDD and HDD resulted in an increased Pan_no in most regions. However, the negative effect of temperature on Grain_no outweighed its positive effect on Pan_no, leading to a decline in yield. This study provides insight for understanding the temperature response of rice yield and its components and will be beneficial for developing targeted adaptations to ensure rice sustainable production under global warming.

温度变化对水稻（Oryza sativa L.）产量及其组成部分的影响已被广泛记录。然而，大多数现有研究都是基于小规模、短期的田间试验，很少有大规模或长期的影响评估。在此，我们使用校准过的作物环境资源综合（CERES）-水稻模型对中国主要水稻种植区的六个气候区进行了数值模拟，时间跨度为 1989-2018 年。利用模拟结果，采用面板模型估算了各气候区水稻产量的温度敏感性，并将产量敏感性分解为单位面积圆锥花序数（Pan_no）、每圆锥花序灌浆粒数（Grain_no）和粒重（Grainwt）三部分的温度敏感性。结果表明，所有气候区的水稻产量都表现出负的温度敏感性，在大多数地区主要是由 Grain_no 的温度敏感性驱动的。此外，谷粒重量在所有地区都不随温度变化。进一步分析表明，产量、Pan_no 和 Grain_no 对高温度日 (HDD) 的敏感性高于对生长度日 (GDD) 的敏感性。在温度较高的情况下，HDD 的增加对 Grain_no 的减少起主导作用，而 GDD 和 HDD 的共同作用则导致大多数地区 Pan_no 的增加。然而，温度对 Grain_no 的负面影响超过了对 Pan_no 的正面影响，导致产量下降。这项研究为了解水稻产量及其组成的温度响应提供了深入的见解，将有利于制定有针对性的适应措施，确保水稻在全球变暖条件下的可持续生产。

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引用次数: 0

Understanding increased grain yield and water use efficiency by plastic mulch from water input to harvest index for dryland maize in China’s Loess Plateau 从水量投入到收获指数，了解中国黄土高原旱地玉米塑料地膜的粮食增产和用水效率

IF 4.5 1区农林科学 Q1 AGRONOMY

European Journal of Agronomy

Pub Date : 2024-11-02 DOI: 10.1016/j.eja.2024.127402

Naijiang Wang , Xiaosheng Chu , Jinchao Li , Xiaoqi Luo , Dianyuan Ding , Kadambot H.M. Siddique , Hao Feng

In China’s Loess Plateau, plastic mulch (PM) is an effective agronomic practice for dryland maize (Zea mays L.) to increase grain yield (GY) and water use efficiency (WUE) under water-limited conditions. However, there is dearth of quantitative data on how PM affects field water use step by step, subsequently increasing GY and WUE. The study aimed to identify which changes in the field water use pathway generated the positive effects of PM on GY and WUE. During the early vegetative stage (EVS), late vegetative stage (LVS), reproductive stage (RS), and entire growing season (GS), the field water use pathway was divided into five sequential steps: total water input (TWI), evapotranspiration to TWI ratio (ET/TWI), transpiration to ET ratio (T/ET), transpiration efficiency (TE), and harvest index (HI). A seven-year field experiment demonstrated that although TWI_GS exhibited no change, TWI_LVS and TWI_RS increased by 6.7 % and 5.4 %, respectively, on average following PM application. This highlighted the PM’s ability to increase water input into fields. Overall, PM negatively, neutrally, and positively affected ET/TWI_EVS (−29.8 %), ET/TWI_LVS, and ET/TWI_RS (+23.9 %), respectively, and thereby made unchanged ET/TWI_GS. There were average increases of 83.3 %, 29.8 %, 26.1 %, and 33.9 % by PM for T/ET_EVS, T/ET_LVS, T/ET_RS, and T/ET_GS respectively. Therefore, increased diversion of inputted water to T occurred in fields with PM. TE positively responded to PM during the LVS and RS. PM increased TE_LVS by 20.9 % and TE_RS by 44.1 % on average, signifying increased aboveground biomass produced per unit T under PM. The proportion of aboveground biomass partitioned to grains remained unaffected by PM as indicated by the neutral response of HI to PM. Increased water input into fields, diversion of inputted water to T, and aboveground biomass produced per unit T contributed to increased GY (+19.9 %) and WUE (+20.0 %) after applying PM. The study enhances our understanding of improved field water use pathway to produce more grains using limited water supplies in PM-based drylands in China’s Loess Plateau and similar regions worldwide.

在中国黄土高原，塑料地膜（PM）是旱地玉米（Zea mays L.）在限水条件下提高籽粒产量（GY）和水分利用效率（WUE）的有效农艺措施。然而，关于 PM 如何逐步影响田间用水，进而提高 GY 和 WUE 的定量数据却十分匮乏。本研究旨在确定在田间用水途径中哪些变化产生了可吸入颗粒物对生长总重和水分利用效率的积极影响。在无性系初期（EVS）、无性系后期（LVS）、生殖期（RS）和整个生长季（GS）期间，田间水分利用途径分为五个连续步骤：总水输入量（TWI）、蒸散量与 TWI 的比率（ET/TWI）、蒸腾量与蒸散量的比率（T/ET）、蒸腾效率（TE）和收获指数（HI）。一项为期七年的田间试验表明，虽然总蒸渗量没有变化，但在施用 PM 后，总蒸渗量和总蒸腾速率平均分别增加了 6.7% 和 5.4%。这凸显了 PM 增加田间水分输入的能力。总体而言，可吸入颗粒物分别对蒸散发/蒸腾水位（-29.8 %）、蒸散发/蒸腾水位（TWILVS）和蒸散发/蒸腾水位（TWIRS）（+23.9 %）产生负面、中性和正面影响，从而使蒸散发/蒸腾水位（TWIGS）保持不变。可吸入颗粒物对蒸散发/蒸腾水位（T/ETEVS）、蒸散发/蒸腾水位（T/ETLVS）、蒸散发/蒸腾水位（T/ETRS）和蒸散发/蒸腾水位（T/ETSS）的平均影响分别增加了 83.3 %、29.8 %、26.1 % 和 33.9 %。因此，在有 PM 的田块中，向 T 输入的水分流增加。在 LVS 和 RS 期间，TE 对 PM 有积极反应。PM 使 TELVS 平均增加了 20.9%，TERS 平均增加了 44.1%，这表明 PM 增加了单位 T 的地上生物量。HI对 PM的中性反应表明，分配给谷物的地上生物量比例不受PM的影响。在施用 PM 后，增加的田间水输入量、输入水对 T 的分流以及单位 T 产生的地上生物量都有助于增加 GY（+19.9 %）和 WUE（+20.0 %）。这项研究加深了我们对改进田间用水途径的理解，从而在中国黄土高原和全球类似地区以可吸入颗粒物为基础的旱地利用有限的水源生产更多的谷物。

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引用次数: 0

Growth conditions but not the variety, affect the yield, seed oil and meal protein of camelina under Mediterranean conditions 地中海条件下荠菜的产量、籽油和粕蛋白受生长条件（而非品种）的影响

IF 4.5 1区农林科学 Q1 AGRONOMY

European Journal of Agronomy

Pub Date : 2024-11-02 DOI: 10.1016/j.eja.2024.127424

N. Codina-Pascual , C. Cantero-Martínez , M.P. Romero-Fabregat , G. De la Fuente , A. Royo-Esnal

European agriculture policies emphasize the importance of agricultural sustainability, focusing on increase of biodiversity through crop diversification. In Mediterranean dryland cropping systems, the introduction of crops in rotation with cereals is challenged by scarce precipitation and high evapotranspiration. In this scenario, camelina (Camelina sativa (L.) Crantz), a low-input annual oleaginous crop with a high morphological plasticity, short life cycle, and interesting oil and meal composition, could be an option to be included in rotation with winter cereals. The aim of this experiment was to study the agronomic performance, and seed oil and meal protein contents of camelina in two different climatic conditions, with a sowing delay in one of them. Several trials were conducted in Montargull (Mediterranean semihumid) and in Lleida (Mediterranean semiarid) in two seasons (2020–21 and 2021–22). In Montargull, two sowing dates (November, SD1 and January, SD2) were established. In each growing condition, three spring camelina varieties were sown (Calena, CO46 and GP204). Camelina was harvested between May and July, and yield and harvest index were measured. After cold pressing the seeds, seed oil and meal protein contents were analysed. Camelina yield and quality was not related to the variety, but to two climatic scenarios: 1) a favourable rainfall distribution without important drought periods (2020–21); 2) significant rainfalls in November and April, but with a drought period in between (2021–22). In the first situation, camelina production ranged from 1533 to 2187 kg ha⁻¹, with high seed oil (40.4–41.4 %) and meal protein (41.0–44.8 %) contents. In the second situation, the yield decreased to 242–661 kg ha⁻¹, seed oil content to 31.0–34.7 %, and meal protein content to 37.6–40.4 %. Despite these seasonal differences, SD1 in Montargull obtained higher average yields and protein content than in Lleida and in SD2. In contrast, in Lleida and in SD2 in Montargull camelina produced higher oil content. The implementation of camelina into Mediterranean dryland crop rotation systems is feasible. Considering the importance of moisture in these climatic conditions, the use of no-till practices is recommended in dryland fields to avoid excessive water loss, while the use of camelina in irrigated fields could be explored. However, more long-term agronomic and industrial research is still needed.

欧洲农业政策强调农业可持续性的重要性，重点是通过作物多样化来增加生物多样性。在地中海旱地种植系统中，引入与谷物轮作的作物面临着降水稀少和蒸散量大的挑战。在这种情况下，荠菜（Camelina sativa (L.) Crantz）--一种低投入的一年生油料作物，具有形态可塑性强、生命周期短、油脂和粕类成分丰富等特点，可以作为与冬季谷物轮作的一种选择。本试验的目的是研究荠菜在两种不同气候条件下的农艺性能、籽油和粕蛋白含量，其中一种气候条件下荠菜的播种会推迟。在蒙塔古尔（地中海半湿润地区）和莱里达（地中海半干旱地区）分两季（2020-21 年和 2021-22 年）进行了多项试验。在 Montargull，确定了两个播种日期（11 月，SD1；1 月，SD2）。在每种生长条件下，播种三个春季荠菜品种（Calena、CO46 和 GP204）。荠菜在 5 月至 7 月间收获，并测量产量和收获指数。冷榨种子后，分析籽油和粕蛋白含量。荠菜的产量和质量与品种无关，而是与两种气候情景有关：1）降雨分布有利，没有重要的干旱期（2020-21 年）；2）11 月和 4 月降雨量大，但中间有干旱期（2021-22 年）。在第一种情况下，荠菜产量为每公顷 1533 至 2187 千克，籽油（40.4-41.4%）和籽粉蛋白（41.0-44.8%）含量较高。在第二种情况下，产量降至 242-661 千克/公顷，籽油含量降至 31.0-34.7%，籽粉蛋白含量降至 37.6-40.4%。尽管存在这些季节性差异，蒙塔尔格尔的 SD1 的平均产量和蛋白质含量仍高于莱里达和 SD2。相比之下，莱里达和蒙塔尔谷 SD2 的荠菜含油量更高。在地中海旱地轮作系统中种植荠菜是可行的。考虑到水分在这些气候条件下的重要性，建议在旱地使用免耕方法，以避免水分过度流失，同时可以探索在灌溉田中使用荠菜。不过，还需要进行更长期的农艺学和工业研究。

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引用次数: 0

Wheat growth stage identification method based on multimodal data 基于多模态数据的小麦生长阶段识别方法

IF 4.5 1区农林科学 Q1 AGRONOMY

European Journal of Agronomy

Pub Date : 2024-11-02 DOI: 10.1016/j.eja.2024.127423

Yong Li , Yinchao Che , Handan Zhang , Shiyu Zhang , Liang Zheng , Xinming Ma , Lei Xi , Shuping Xiong

Accurate identification of crop growth stages is a crucial basis for implementing effective cultivation management. With the development of deep learning techniques in image understanding, research on intelligent real-time recognition of crop growth stages based on RGB images has garnered significant attention. However, the small differences and high similarity in crop morphological characteristics during the transition between adjacent growth stages pose challenges for accurate identification. To address this issue, this study proposes a multi-scale convolutional neural network model, termed MultiScalNet-Wheat (MSN-W), which enhances the algorithm's ability to learn complex features by utilizing multi-scale convolution and attention mechanisms. This model extracts key information from redundant data to identify winter wheat growth stages in complex field environments. Experimental results show that the MSN-W model achieves a recognition accuracy of 97.6 %, outperforming typical convolutional neural network models such as VGG19, ResNet50, MobileNetV3, and DenseNet. To further address the difficulty in recognizing growth stages during transition periods, where canopy morphological features are highly similar and show small differences, this paper introduces an innovative approach by incorporating sequential environmental data related to wheat growth stages. By extracting these features and performing multi-modal collaborative inference, a multi-modal feature-based wheat growth stage recognition model, termed MultiModalNet-Wheat (MMN-W), is constructed on the basis of the MSN-W model. Experimental results indicate that the MMN-W model achieves a recognition accuracy of 98.5 %, improving by 0.9 % over the MSN-W model. Both the MSN-W and MMN-W models provide accurate methods for observing wheat growth stages, thereby supporting the scientific management of winter wheat at different growth stages.

准确识别作物生长阶段是实施有效栽培管理的重要基础。随着图像理解领域深度学习技术的发展，基于 RGB 图像的作物生长阶段智能实时识别研究受到了广泛关注。然而，作物形态特征在相邻生长阶段过渡期间的微小差异和高度相似性给准确识别带来了挑战。为解决这一问题，本研究提出了一种多尺度卷积神经网络模型，称为多尺度卷积神经网络-小麦（MSN-W），该模型利用多尺度卷积和注意力机制，增强了算法学习复杂特征的能力。该模型从冗余数据中提取关键信息，以识别复杂田间环境中的冬小麦生长阶段。实验结果表明，MSN-W 模型的识别准确率达到 97.6%，优于 VGG19、ResNet50、MobileNetV3 和 DenseNet 等典型卷积神经网络模型。在过渡时期，冠层形态特征高度相似且差异较小，为了进一步解决生长阶段识别困难的问题，本文引入了一种创新方法，即结合与小麦生长阶段相关的连续环境数据。通过提取这些特征并进行多模态协同推理，在 MSN-W 模型的基础上构建了一个基于多模态特征的小麦生长阶段识别模型，称为多模态网络-小麦（MMN-W）。实验结果表明，MMN-W 模型的识别准确率达到 98.5%，比 MSN-W 模型提高了 0.9%。MSN-W 和 MMN-W 模型都为观测小麦生长阶段提供了准确的方法，从而为冬小麦不同生长阶段的科学管理提供了支持。

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引用次数: 0

Combination with moderate irrigation water temperature and nitrogen application rate enhances nitrogen utilization and seed cotton yield 结合适度的灌溉水温和施氮量，提高氮素利用率和籽棉产量

IF 4.5 1区农林科学 Q1 AGRONOMY

European Journal of Agronomy

Pub Date : 2024-10-31 DOI: 10.1016/j.eja.2024.127417

Zhanli Ma , Jing He , Jinzhu Zhang , Wenhao Li , Feihu Yin , Yue Wen , Yonghui Liang , Hanchun Ye , Jian Liu , Zhenhua Wang

To promote the efficient utilization of groundwater and improve nitrogen fertilizer effectiveness, a reasonable range of nitrogen application rates and irrigation water temperature was investigated. A field experiment was conducted in Xinjiang, China, in 2022 and 2023, involving four irrigation water temperature levels (T0: 15 °C, T1: 20 °C, T2: 25 °C, and T3: 30 °C) and three nitrogen application rates (F1: 250 kg ha⁻¹, F2: 300 kg ha⁻¹, and F3: 350 kg ha⁻¹). The results indicated that soil nitrogen content, cotton dry matter weight, cotton nitrogen content, seed cotton yield, and nitrogen partial factor productivity (NPFP) increased with higher nitrogen application rates. However, as irrigation water temperature increased, soil nitrogen content decreased, whereas cotton dry matter weight, cotton nitrogen content, seed cotton yield, and NPFP initially increased before declining. Notably, the maximum yield and NPFP among all treatments were observed in T2F2 (25 °C, 300 kg ha⁻¹), yielding 6652 kg ha^–1 and 6941 kg ha^–1, and in T2F1 (25 °C, 250 kg ha^–1), with 24.20 kg kg^–1 and 25.20 kg kg^–1 in 2022 and 2023, respectively. Furthermore, the optimal range of irrigation water temperature of 23.82–27.41 °C and nitrogen application rate of 276.43–289.23 kg ha^–1 were identified to achieve over 80 % of the highest seed cotton yield and NPFP using multiple regression and spatial analysis methods. This study offers valuable guidance for optimizing irrigation and fertilization strategies to enhance resource efficiency and promote sustainable cotton production in arid regions.

为促进地下水的高效利用，提高氮肥肥效，研究了合理的氮肥施用量和灌溉水温范围。2022 年和 2023 年，在中国新疆进行了一项田间试验，涉及四种灌溉水温水平（T0：15 °C、T1：20 °C、T2：25 °C、T3：30 °C）和三种施氮量（F1：250 kg ha-1、F2：300 kg ha-1、F3：350 kg ha-1）。结果表明，土壤含氮量、棉花干物质重量、棉花含氮量、籽棉产量和氮部分要素生产率（NPFP）随着施氮量的增加而提高。然而，随着灌溉水温的升高，土壤含氮量下降，而棉花干物质重量、棉花含氮量、籽棉产量和氮部分要素生产率则先上升后下降。值得注意的是，在所有处理中，T2F2（25 °C，300 千克/公顷）和 T2F1（25 °C，250 千克/公顷）产量最高，分别为 6652 千克/公顷和 6941 千克/公顷；在 2022 年和 2023 年，T2F2（25 °C，300 千克/公顷）和 T2F1（25 °C，250 千克/公顷）产量最高，分别为 24.20 千克/公顷和 25.20 千克/公顷。此外，利用多元回归和空间分析方法，确定了灌溉水温的最佳范围为 23.82-27.41 °C，施氮量为 276.43-289.23 kg ha-1，以实现 80% 以上的最高籽棉产量和 NPFP。这项研究为优化灌溉和施肥策略，提高资源利用效率，促进干旱地区棉花可持续生产提供了宝贵的指导。

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