European Journal of Agronomy最新文献

Improvements of soil organic carbon and reduction of carbon footprint are critical for the sustainability of agricultural production system. In a 14-year (2006–2020) field experiment, we assessed the effects of conservational (reduced/zero) tillage and residue management (incorporation/retention) (CsT+RM) practices on carbon input, carbon sequestration, productivity and profitability rice-wheat system (RWS) in western Indo-Gangetic Plains (IGP) of India. Experiment consisted one scenario of conventional tillage (Sc-1: Puddle transplanted rice - conventional tilled wheat); and four scenarios of CsT+RM that are, Sc-2: Reduce tilled direct seeded rice (RTDSR) - reduce tilled wheat (RTW); Sc-3: RTDSR-RTW + 1/3rd residue incorporation (RI); Sc-4: Zero tilled direct seeded rice (ZTDST)-zero tilled wheat (ZTW); and Sc-5: ZTDSR-ZTW + 1/3rd residue retention (RR). Overall, 14-years mean DSR yield significantly (p < 0.05) lowered (9.0–22.0 %), and wheat yield significantly increased (4.4–9.2 %) in CsT+RM practices as compared to Sc-1. The mean RWS yield lowered by 1.0–3.8 % in reduced tillage and 6.3–9.3 % in zero tillage, along with 10.9–17.4 % lower cost of cultivation and nonsignificant higher return over variable cost under CsT+RM practices. The sustainable yield index of DSR was lower (0.50–0.58), and wheat was higher (SYI; 0.65–0.69) in indicating the low sustainability of DSR and better sustainability of wheat in CsT+RM. The long-term CsT+RM caused net enrichment in SOC stock by 2.4–21.0 %, and carbon sequestration from 9.9 % to 87.0 % in different scenarios over Sc-1. In order to counterbalance the loss of SOC and maintain its level, a critical amount of 1.17 Mg C ha⁻¹ yr⁻¹ need to be added into the soil. The CsT+RM thus enhanced the SOC stock and sequestration in the soil and provided at par system yield in reduced tillage and lower yield in zero tillage grown RWS. Further, better management of DSR including development of suitable genotype for direct seeding, ensuring uniform crop establishment, weed and micronutrient management under reduced/zero tillage is needed for long-term sustainability of DSR-ZTW system in the western IGP of India.

Timely and precise farm inspection, which involves the identification and recognition of harmful insects and diseases, is crucial for safeguarding crop production. Traditional vision-based pest recognition methods typically require extensive annotated data for each pest species and a lengthy training process. This approach is time-consuming, labor-intensive, and prone to human error. Zero-shot learning offers a potential solution by enabling pest segmentation and control without requiring explicit training data. This study supports farmers in automatically identifying ten common pests and their precise locations in real-world outdoor environments. The zero-shot pest segmentation is based on a hybrid approach combining Explainable Contrastive Language-Image Pre-training (ECLIP) and Segment-Anything (SAM). Moreover, an optimized super-resolution model and various data augmentation methods are implemented to improve the quality of the dataset. Lastly, a mask post-processing step is applied to remove highly overlapping segmented masks and noise blobs caused by the complex background. The mean Intersection over Union (mIoU) of 66.5 % on the validation set demonstrates the potential of zero-shot methods for automated pest segmentation during farm inspections. This research lays the foundation for accurate pest monitoring systems capable of adapting to new pests, ultimately improving agricultural productivity.

Accurate identification and monitoring of sunflower capitula are crucial for field phenotypic analysis, cultivation management, phenological monitoring, and yield prediction. Manual observation, however, faces significant challenges due to the complexity of field environments and the morphological diversity of sunflower capitula. Unmanned Aerial Vehicles (UAVs) have emerged as an ideal platform for monitoring sunflower capitula due to their low cost and high spatiotemporal resolution. This study introduces Sunflower-YOLO, an enhanced model based on YOLOv7-tiny, designed for detecting sunflower capitula in UAV remote sensing images. The model effectively identifies sunflower capitula and distinguishes between three specific states: open, half-open, and bud. Sunflower-YOLO incorporates several key improvements: the SiLU activation function replaces the original LeakyReLU, enhancing the model’s nonlinear expression capability; a shallow high-resolution feature map and an additional detection head for small targets are introduced during the feature fusion stage to improve the detection performance of small capitula; and the integration of deformable convolution and the SimAM attention mechanism enhances the ELAN structure in the backbone, creating a new DeformAtt-ELAN structure that improves the model’s ability to capture morphological variations and reduces noise interference. Experimental results demonstrate that Sunflower-YOLO achieves precision, recall, and [email protected] of 92.3 %, 89.7 %, and 93 %, respectively, marking improvements of 4.2 %, 4.2 %, and 3.7 % over the original YOLOv7-tiny model. The average precision (AP) for the three growth states is 98.7 %, 93.4 %, and 87 %, with AP for the half-open and bud states improving by 6.5 % and 4.7 %, respectively. The model’s FLOPs is 17.7 G, its size is 13.8MB, and it achieves an FPS of 188.52. Compared to current mainstream state-of-the-art (SOTA) models for object detection, Sunflower-YOLO achieves the highest [email protected] in detecting multiple types of sunflower capitula. The constructed capitulum density map offers a practical view for observing sunflower growth status. This study highlights the immense potential of combining UAV remote sensing technology with YOLO object detection algorithms in monitoring sunflower capitula and their growth processes, providing an innovative and effective approach for precision agriculture practices.

Contemporary crop research programs involve the evaluation of numerous micro-plots spread across extensive experimental fields. As a result, there is a growing need to depart from labor-intensive manual measurements when assessing phenological data. The growing significance of high throughput phenotyping platforms (HTTP), including unmanned aerial vehicles (UAVs), has rendered these technologies essential in crop research. The overall objective of this study is to explore and validate the use of HTTP methodologies, specifically the potential of vegetation indices (VIs) derived from conventional RGB images, to forecast the date of heading (DH) and maturity (DM) for various cereal crops under different irrigation conditions. To pinpoint DH and DM prediction, a total of nine UAV surveys were conducted throughout the entire crop cycle. Prediction models for DH and DM using VIs were successfully developed for various crop species, explaining 65 % of the variance in bread wheat and 75 % in oats. The highest percentages of variance explained were achieved when models were developed separately for the two irrigation conditions (well-irrigated and rainfed). However, the percentage of variance explained by these models decreased when applied to barley (R²<0.5 for DH). Notably, including final plant height as a predictor increased the percentage of variance explained by the models only for irrigated bread wheat. Furthermore, the utilization of multi-temporal equations, which amalgamated data from diverse UAV surveys, notably enhanced the percentage of variance explained by the model (+160.71 % improvement in DH predictions), particularly those tailored to each specific crop species and irrigation condition. The investigation additionally established a thorough protocol for modeling the phenological aspects of cereal crops utilizing data acquired from UAVs, thereby enhancing the accessibility of this technology for measurements of phenology in large crop research programs.

Organic mulching is a well-known management practice that conserves soil water and nutrients as well as increases crop yield. Nonetheless, research on combined organic mulching using oil palm empty fruit bunch (EFB) and irrigation is limited. Field-based experiments were conducted over three seasons to test the sole and combined effects of EFB as organic mulch and irrigation on the growth, total dry matter yield (TDMY), accumulated intercepted photosynthetically active radiation (AIPAR), and radiation use efficiency (RUE) of African eggplant (Solanum aethiopicum L.) in a low fertile tropical sandy clay loam soil. Air-dried EFB was used as an organic mulch by spreading it on the soil surface at rates of 0 (EFB₀), 20 (EFB₂₀), and 40 t ha⁻¹ (EFB₄₀), and either fully-irrigated (I₁₀₀), deficit-irrigated (I₄₀), or non-irrigated (I₀). The I₁₀₀ plots were irrigated to field capacity (FC) every 3–4 days based on PR2 Profile Probe measurements and the resultant irrigation volume supplied to the plants via drip irrigation tubes. The I₄₀ plots received 40 % of the water given to the I₁₀₀ plots, and the I₀ plots were solely rain-fed. At the end of the third season, the 40 t ha⁻¹ EFB-mulch increased soil pH, electrical conductivity (EC), soil organic carbon, potassium, cation exchange capacity, and the soil’s specific surface area. In the first season, all the measured eggplant growth and yield parameters were neither responsive to irrigation only, EFB-mulch only, or both. In the second and third seasons, the EFB₂₀ and EFB₄₀ treatments significantly (p < 0.05) increased leaf chlorophyll content index (LCCI), photosystem II (Fv/Fm ratio), absolute performance index (PI_abs), TDMY, AIPAR, and RUE compared to the non-mulched control treatment. Soil pH was high in the EFB-mulched plots and correlated positively with TDMY and AIPAR. The I₁₀₀ significantly improved LCCI, Fv/Fm, PI_abs, and TDMY during the second season. In the third season, a highly significant interaction between irrigation and mulching was detected on TDMY, AIPAR, RUE, LCCI, Fv/Fm ratio, and pH (H₂O). This indicated a positive effect on soil nutrient availability especially phosphorus as TDMY and AIPAR correlated with soil pH. The I₁₀₀ and I₄₀ significantly increased AIPAR by 48.1 % and 37.2 %, and RUE by 26.7 % and 11.0 %, respectively, compared to I₀ during the third season. The total dry matter yield of the African eggplant was enhanced by EFB-mulch, with the effect increasing over up to three growing seasons, especially when combined with irrigation during dry periods.

Nitrogen (N) deficiency has detrimental effects on productivity and the profit of producers in areas where signal grass [Urochloa decumbens (Stapf) R.D. Webster (syn. Brachiaria decumbens Stapf.)] cv. Basilisk is grown for seed production. The objective of this paper was to clarify the effects of indicators of signal grass plant N status on seed yield (SY), SY components, yield formation, seed quality, panicle growth parameters, and remobilization of vegetative N on seed growth. Germinable pure SY, harvest index (HI), and N harvest index (NHI) were also measured. Different rates of N fertilizer application (0, 50, 100, and 150 kg ha⁻¹) were applied after the cleaning cut to both the first crop (October - January) and the second crop (February - May) in 2010–2011 and 2011–2012, on a sandy loam soil representative of soils used for seed production in Brazil. Although the N nutrition index (NNI) increased at key developmental stages, the highest values were near to 0.85. This suggests that all crops were maintained under N-limiting conditions. In N-limited crops, a strong relationship was detected between NNI and accumulated N deficit throughout the study period with relative SY. A low NNI after the cleaning cut was found to restrict fertile tiller number (FTN), spikelets per panicle, and spikelet density m⁻² measured at anthesis. In all crops, at harvest, NNI at anthesis increased germinable pure SY, FTN, number of seeds per panicle, HI, NHI, and amount of remobilized N to seeds, but not thousand seed weight (TSW), seed germination, panicle dry matter (DM) accumulation rate, and individual seed growth rate. Regression analyses suggested that the NNI, accumulated N deficit, aboveground plant biomass (AGPB), and N content were better associated with relative SY than with plant N concentration (PNC). The study shows that the NNI quantifies the intensity and duration of N deficiency in signal grass and should be considered in research studies and for application in seed production fields to improve N fertilization recommendations.

Plant stem is essential for the delivery of resources and has a great impact on plant lodging resistance and yield. However, how to accurately and efficiently extract structural information from crop stems is a big headache. In this study, we first established a Maize Stalk Cross-section Phenotype (MSCP) dataset containing anatomical information of 990 images from hand-cut transections of stalks. Then, to large-scale measure the stalk anatomy features, we developed a Maize Stalk Cross-section Phenotyping Network (MSCP-Net) which integrated a convolutional neural network and the methods of instance segmentation and key point detection. A total of 14 stalk anatomical parameters (traits) can be automatically produced with high [email protected] (0.907) for the parameter “vascular bundles segmentation” and high DICE (0.864) for the parameter “functional zones segmentation”. The cross-validation with the MSCP dataset indicated the good performance of MSCP-Net in predicting anatomical traits. On this basis, the correlation analysis across 14 anatomical traits and 12 agronomic importance traits in 110 maize inbred-lines was conducted and revealed that the stalk related traits (stem cross-section, large vascular bundles, fiber contents, and aerial roots) are key indicators for lodging resistance and grain yield of maize. In addition, the maize inbred-lines were classified into two groups, and the higher value of group II compared with group I in breeding hybrid varieties was discussed. The results demonstrated that the MSCP-Net is expected to be a useful tool to rapidly obtain stem anatomical traits which are agronomic important in maize genetic improvement.

While crop models are increasingly applied to large-scale areas, inadequate observation data make it difficult to calibrate the model’s phenological parameters at the regional scale. The present study proposed a simple mathematical method for estimating rice phenological parameters across spatial scales for the ORYZA model. The method establishes the cumulative function of the phenological parameters (CFPP). By fitting the CFPP with the so-called growth curve, the 4 phenological parameters of the ORYZA model were transformed into 3 fitted parameters in the equation of CFPP. Functions between the fitted parameters and several meteorological and field management factors were established. These established functions were substituted back into CFPP to construct a modified CFPP. Due to the inter-translational relationship between CFPP and the original phenological parameters, the values of phenological parameters could be estimated by the modified CFPP based on meteorological and field management factors. The newly proposed mathematical method was applied in the Yangtze River Basin (YRB), China. The results indicated that the multi-station average of the absolute value of relative errors for the rice panicle initiation, flowering, and maturity dates within the YRB were 12.3 %, 10.5 %, and 8.7 %, respectively, which were at most 4.8 % larger than that simulated using parameters calibrated using each station’s phenological data. The phenological parameters estimated by the novel mathematical method had close performance to those calibrated directly based on observed data at most stations in terms of rice phenology simulation. The present study provided a new solution for phenological parameter calibration for crop models when applied in a large-scale area.

Mixed farming systems play a crucial role in Australian agriculture, offering economic, social, and environmental advantages. However, these systems are vulnerable to climate change, characterized by rising temperatures and increased rainfall variability. We utilized the pre-calibrated AusFarm model, forced with daily climate data downscaled from 27 Global Climate Models, to simulate how climate change would affect mixed-farming systems at two sites, Condobolin and Wagga Wagga located in southeastern Australia. The results indicated that climate change had diverse effects on crop yields. The simulated yield for some crops, such as canola, was projected to decrease, while others, like field peas, were expected to increase. Elevated atmospheric CO₂ levels were anticipated to boost pasture production, but the overall outcome would depend on how these changes interact with rising temperatures and changed rainfall patterns. The increase in pastures was associated with higher live sheep weights and increased fleece growth, with a more significant impact observed at the drier Condobolin site. Furthermore, we found that the gross margin was projected to rise at both sites, with Condobolin experiencing more variability under the influence of climate change. These modelling findings highlight the capacity of mixed-farming systems, which integrate both crops and livestock, to uphold or even improve farm profitability in the context of impending climate change. This underscores the crucial significance of mixed-farming systems in southeastern Australia.

The impact of excessive rainfall or waterlogging on maize growth and yield have been widely studied, but the effects of planting density and N management under waterlogging remain unknown. We observed the changes in maize yield caused by excessive rainfall via a short-term experiment (2017 to present) in Changchun (125°14.231′–125°14.914′ E, 43°56.603′–43°57.274′ N), China. The experiment was conducted at four planting densities (45,000, 60,000, 75,000 and 90,000 plants/ha) and three nitrogen (N) rates (120, 180, and 240 kg/ha). The objective was to explore the effect of excessive precipitation on maize yield through changes in maize growing conditions, and the uptake, allocation, and utilization of N under different planting densities and N rates from 2019 to 2022. The precipitation during the whole growth period of maize in 2019 (542.9 mm) and 2020 (560.0 mm) was normal, while it was excessive in 2021 (829.10 mm) and 2022 (953.56 mm), especially during the vegetative stage from V12 to VT (355.60–482.10 mm). Excessive rainfall negatively affected the growth, photosynthetic characteristics (Pn: −20.00 %, SPAD: −50.50 %), absorption (−56.86 %), distribution (−15.83 %), N utilization efficiency (NUE: −29.69 %), and grain yield (−44.67 %) of maize. Our results indicate that yield loss was minimized (−22.88 %) when the planting density was appropriately reduced (from 75,000 to 60,000 plants/ha) and the N rate was increased from 180 to 240 kg/ha. The effect of different waterlogging durations on yield exhibited a significantly negative linear relation (R² > 0.80). This study revealed the physiological mechanism of the sustained effects of excessive rainfall on maize growth and yield. Waterlogging significantly affected the SPAD of maize (p < 0.01, R² = 0.04), resulting in insufficient kernel N content (p < 0.001, R² = 0.16) and decreased NUE (p < 0.001, R² = 0.48). These factors significantly affected yield and exerted a significant negative correlation with planting density (p < 0.05). Our findings improved understanding of planting density and N management for growth and yield of maize under excessive rainfall conditions in mid-high latitude agriculture areas of the world.