Crop Growth Period Research Articles

Drip irrigation and mulching reduce weed interference and improve water productivity of spring maize

Abstract Drip irrigation and mulching were tested to minimize unproductive water loss through evaporation and weed interference. A field experiment was conducted during spring season of 2020 and 2021 in split plot design with three replications. The study includes six treatment combinations of drip irrigation methods (surface drip and subsurface drip irrigation) and mulching (black plastic, paddy straw and no mulch) along with one conventional furrow irrigation without mulching (as control) in main plots. Four weed control treatments (atrazine 1000 g a.i./ha as pre-emergence, two hand weedings at 30 and 60 days after sowing [DAS], weed free and weedy for whole crop growth period) were kept in the subplots. The combination of drip irrigation and mulches significantly enhanced leaf area index and crop biomass at 60 DAS than furrow irrigation. Integration of subsurface drip irrigation with plastic mulching resulted in the lowest weed density and biomass among main plots. Drip irrigation coupled with plastic and straw mulching resulted in 86 and 50% reduction in weed density and biomass, respectively, as compared to no mulching. Integration of subsurface drip with paddy straw mulch and black plastic mulch resulted in 17.1 and 15.5% higher maize grain yield, respectively, as compared to furrow irrigation. The highest irrigation water productivity (3.58 kg/m3) was observed in combination of subsurface drip and paddy straw mulch followed by combination of subsurface drip and black plastic mulch (3.51 kg/m3). Overall, straw mulching in drip irrigation system proved economical in terms of maize productivity.

Effect of Land Configuration and Rainfall on Pennisetum glaucum (L.) R. Br. under Saline Irrigation

Field experiment was conducted in Eastern Block Farm of Tamil Nadu Agricultural University, Coimbatore to evaluate the effect of land configuration and rainfall on growth and yield of pearl millet under saline water irrigation in summer, 2024. The study was carried out in split plot design with three main plots (flatbed (M1), ridges and furrows (M2) and raised bed paired row planting (M3)) along with four subplot treatments (direct sowing (S1), 10 Day Old Seedlings (DOS) (S2), 15 DOS (modified dapog) (S3) and 20 DOS (S4)) which was replicated thrice. Water availability is a critical factor in influencing the length of the growing period while water quality significantly influences crop growth and yield. Consequently, rainfall received during the growing period affects plant height, Leaf Area Index (LAI), effective tillers and yield. The cumulative rainfall received 208 mm in 22 days and with 10 rainy days during the crop growth period. Rainfall between 40 to 60 Days After Sowing/Planting (DAS/P) corresponded with Crop Growth Rate (CGR). Among main plot treatments, ridges and furrows (M2) recorded higher plant height (173 cm) and effective tillers (2.6) at harvest. Grain and stover yields (t ha-1) were also higher in ridges and furrows (M2) (3.31 and 6.12, respectively) which was statistically on par with raised bed paired row planting (M3) (3.31 and 6.12, respectively).

Simulation of water-salt transport and balance in cultivated-wasteland system based on SWAP model in Hetao Irrigation District of China

Water and salt transport among different use type land is important to irrigation management in arid area. In this study, a typical irrigation unit including cultivated land and wasteland in Hetao Irrigation District of China was selected to explore water and salt transport between cultivated land and wasteland system. Soil water-salt dynamics, groundwater depth and salinity were observed within the period of crop growing and autumn irrigation period in 2018 and 2019. Calibrated SWAP model was used to simulate water and salt flux of cultivated land and wasteland during the crop growing period and autumn irrigation period. Furthermore, water-salt transport exchange capacity was calculated between cultivated land and wasteland system in study area. Groundwater was recharged by soil water and soil salt was leached in cultivated land during the crop growing period. Soil water was recharged by groundwater and soil salt was accumulated in saline wasteland during the crop growing period. During the crop growing period, the total leaching of soil salinity was 15.64 t·ha−1 in cultivated land. Soil salt accumulation was 2.03 t·ha−1 in saline wasteland. During the autumn irrigation period, the total leaching of soil salinity was 14.93 t·ha−1 in cultivated land. Total leaching of soil salinity was 1.56 t·ha−1 in saline wasteland. Overall, saline wasteland had an important role to receive salt from cultivated land and maintain salt dynamic balance in arid irrigation area with shallow groundwater.

Analyzing Temporal Characteristics of Winter Catch Crops Using Sentinel-1 Time Series

Catch crops are intermediate crops sown between two main crop cycles. Their adoption into the cropping system has increased considerably in the last years due to its numerous benefits, in particular its potential in carbon fixation and preventing nitrogen leaching during winter. The growth period of catch crops in Germany is often marked by dense cloud cover, which limits land surface monitoring through optical remote sensing. In such conditions, synthetic aperture radar (SAR) emerges as a viable option. Despite the known advantages of SAR, the understanding of temporal behavior of radar parameters in relation to catch crops remains largely unexplored. Hence, in this study, we exploited the dense time series of Sentinel-1 data within the Copernicus Space Component to study the temporal characteristics of catch crops over a test site in the center of Germany. Radar parameters such as VV, VH, VH/VV backscatter, dpRVI (dual-pol Radar Vegetation Index) and VV coherence were extracted, and temporal profiles were interpreted for catch crops and preceding main crops along with in situ, temperature, and precipitation data. Additionally, we examined the temporal profiles of winter main crops (winter oilseed rape and winter cereals), that are grown parallel to the catch crop growing cycle. Based on the analyzed temporal patterns, we defined 22 descriptive features from VV, VH, VH/VV and dpRVI, which are specific to catch crop identification. Then, we conducted a Kruskal–Wallis test on the extracted parameters, both crop-wise and group-wise, to assess the significance of statistical differences among different catch crop groups. Our results reveal that there exists a unique temporal pattern for catch crops compared to main crops, and each of these extracted parameters possess a different sensitivity to catch crops. Parameters VV and VH are sensitive to phenological stages and crop structure. On the other hand, VH/VV and dpRVI were found to be highly sensitive to crop biomass. Coherence can be used to detect the sowing and harvest events. The preceding main crop analysis reveals that winter wheat and winter barley are the two dominant main crops grown before catch crops. Moreover, winter main crops (winter oilseed rape, winter cereals) cultivated during the catch crop cycle can be distinguished by exploiting the observed sowing window differences. The extracted descriptive features provide information about sowing, harvest, vigor, biomass, and early/late die-off nature specific to catch crop types. In the Kruskal–Wallis test, the observed high H-statistic and low p-value in several predictors indicates significant variability at 0.001 level. Furthermore, Dunn’s post hoc test among catch crop group pairs highlights the substantial differences between cold-sensitive and legume groups (p < 0.001).

Evaluation of Coloured Sticky Traps for Monitoring the Population of Budfly, Dasyneura lini on Linseed Crop

A field study was conducted at Indira Gandhi Krishi Vishwavidyalaya Raipur, Chhattisgarh Rabi 2022-23 and 2023-24 to evaluate the use of different coloured sticky traps to assess the colour magnestism of budfly infecting linseed. The attractiveness of nine colour yellow, light yellow, blue, light blue, white, green, grey, pink, orange, brown, black and transparent (check) were evaluated during 2022-23 and 2023-24. Among the colour sticky traps average budfly attracted were maximum in the yellow coloured sticky traps during both the seasons with the cumulative value of 14.62 budfly/trap. The highest number of budfly trapped in yellow-coloured sticky trap over the crop growth period than other sticky traps. Lowest number of budfly catches were recorded in black traps in this experiment.

Integrating S1A microwave remote sensing and DSSAT CROPGRO simulation model for groundnut area and yield estimation

This study sought to corroborate microwave remote sensing and simulation models to efficiently delineate groundnut cultivation area and to estimate the yield by integration. Near real-time information on crop acreage and yield estimation is essential for making policy decisions. S1A SAR data were downloaded for entire crop growth period of groundnut during Kharif monsoon seasons (June – October) of 2019 and 2020 and were processed using MAPSCAPE RIICE software to extract groundnut cultivated area in the study districts of Tamil Nadu. Spectral dB curve groundnut generated using multi-date Sentinel 1 A SAR data showed a minimum at sowing, reached a peak at the pod development stage and decreased after that towards maturity. Groundnut area map was generated with a classification accuracy of 85.2 and 84.8 per cent with a kappa coefficient of 0.70, and total groundnut area of 104343 and 116199 ha was mapped during Kharif monsoon season 2019 and 2020, respectively. The mean agreement of 75.01 and 84.94 per cent was observed between DSSAT model simulated LAI and observed LAI at thirty monitoring locations in the study area during Kharif monsoon season 2019 and 2020, respectively, whereas agreement for yield was 82.11 and 83.70 per cent with RMSE of less than 20 per cent. Spatial distribution of groundnut LAI and yield was estimated by assimilating dB from satellite image and from DSSAT model, respectively. The estimated mean spatial LAI was 2.81 and 3.52, whereas mean spatial pod yield was 2124 and 2195 Kg ha−1 during Kharif monsoon season 2019 and 2020, respectively with RMSE of less than 20 per cent and R2 for integrating satellite products and simulation model for spatial estimates during both the year was >0.70, it shows the fitness of products towards increased accuracy of estimation.

Rice night-time thirst: Genotype nutrient needs reflected in nocturnal transpiration

To enhance rice grain protein content, understanding strategies to improve nitrogen uptake is crucial. While the impact of transpiration on nitrogen flux is known in trees, its role in rice is unclear due to inconsistent results. Our study used a phenomics facility for real-time transpiration measurements during the entire crop growth period. We hypothesized that genotypes respond differently to transpiration regulation based on nitrogen needs. This study investigates the morphological responses and grain protein content (GPC) of two genotypes of rice, GEN-RIC_784 and GEN-RIC_384, under varying light and nitrogen conditions. GEN-RIC_784 exhibited lower reductions in biomass and total leaf area under limiting nitrogen and light compared to GEN-RIC_384. Both genotypes showed comparable reduction in biomass and leaf area when low nitrogen was combined with low light (LN + LL) condition. GEN-RIC_784 flowered early under low light, while GEN-RIC_384 did so only in LN + LL conditions. GEN-RIC_384 experienced significant yield reductions under all treatments except LN + LL, while maintaining high GPC compared to control. In contrast, GEN-RIC_784 showed a >50% reduction in GPC under low nitrogen conditions. Cumulative water transpired decreased notably only under LN + LL for both genotypes. GEN-RIC_384 had higher daytime transpiration declines across treatments and increased nighttime transpiration in CN + LL and LN + AL treatments. Daytime transpiration rates per leaf area were higher across treatments compared to controls. Water use efficiency decreased in both genotypes, most prominently under LN + LL. Across growth stages, transpiration trends varied, with notable increases under LN + AL and LN + LL. GEN-RIC_784 showed higher transpiration during vegetative stages, while GEN-RIC_384 showed higher nocturnal transpiration under CN + LL. Nitrogen supplementation affected shoot growth and chlorophyll content, particularly in GEN-RIC_384, with notable reductions when nitrogen was withheld at night. The study underscores the complex genotype-light-nitrogen interactions in rice, offering insights for enhancing rice productivity and grain quality under diverse environmental conditions.

Estimation of Soil Moisture during Different Growth Stages of Summer Maize under Various Water Conditions Using UAV Multispectral Data and Machine Learning

Accurate estimation of soil moisture content (SMC) is vital for effective farmland water management and informed irrigation decision-making. The utilization of unmanned aerial vehicle (UAV)-based remote sensing technology to monitor SMC offers advantages such as mobility, high timeliness, and high spatial resolution, thereby compensating for the limitations of in-situ observations and satellite remote sensing. However, previous research has primarily focused on SMC diagnostics for the entire crop growth period, often neglecting the development of targeted soil moisture modeling paradigms that account for the specific characteristics of the canopy and root zone at different growth stages. Furthermore, the variations in soil moisture status between fields, resulting from the hysteresis of water flow in irrigation channels at different levels, may influence the development of soil moisture modeling schemes, an area that has been seldom explored. In this study, SMC models based on UAV spectral information were constructed using Random Forest (RF) and Particle Swarm Optimization-Support Vector Machine (PSO-SVM) algorithms. The soil moisture modeling paradigms (i.e., input–output mapping) under different growth stages and soil moisture conditions of summer maize were systematically compared and discussed, along with the corresponding physical interpretability. Our results showed that (1) the SMC modeling schemes differ significantly across the various growth stages, with distinct input–output mappings recommended for the early (i.e., jointing, tasselling, and silking stages), middle (i.e., blister and milk stages), and late (i.e., maturing stage) periods. (2) these machine learning-based models performed best at the jointing stage, while subsequently, their accuracy generally exhibited a downward trend as the maize grew. (3) the RF model demonstrates superior robustness in estimating soil moisture status across different fields (moisture conditions), achieving optimal estimation accuracy in fields with overall higher SMC in line with the PSO-SVM model. (4) unlike the RF model’s robustness in spatial SMC diagnostics, the PSO-SVM model more reliably captured the temporal dynamics of SMC across different growth stages of summer maize. This study offers technical references for future modelers in UAV-based SMC modeling across various spatial and temporal conditions, addressing both the types of models as well as their input features.

Towards Optimising the Derivation of Phenological Phases of Different Crop Types over Germany Using Satellite Image Time Series

Operational crop monitoring applications, including crop type mapping, condition monitoring, and yield estimation, would benefit from the ability to robustly detect and map crop phenology measures related to the crop calendar and management activities like emergence, stem elongation, and harvest timing. However, this has proven to be challenging due to two main issues: first, the lack of optimised approaches for accurate crop phenology retrievals, and second, the cloud cover during the crop growth period, which hampers the use of optical data. Hence, in the current study, we outline a novel calibration procedure that optimises the settings to produce high-quality NDVI time series as well as the thresholds for retrieving the start of the season (SOS) and end of the season (EOS) of different crops, making them more comparable and related to ground crop phenological measures. As a first step, we introduce a new method, termed UE-WS, to reconstruct high-quality NDVI time series data by integrating a robust upper envelope detection technique with the Whittaker smoothing filter. The experimental results demonstrate that the new method can achieve satisfactory performance in reducing noise in the original NDVI time series and producing high-quality NDVI profiles. As a second step, a threshold optimisation approach was carried out for each phenophase of three crops (winter wheat, corn, and sugarbeet) using an optimisation framework, primarily leveraging the state-of-the-art hyperparameter optimization method (Optuna) by first narrowing down the search space for the threshold parameter and then applying a grid search to pinpoint the optimal value within this refined range. This process focused on minimising the error between the satellite-derived and observed days of the year (DOY) based on data from the German Meteorological Service (DWD) covering two years (2019–2020) and three federal states in Germany. The results of the calculation of the median of the temporal difference between the DOY observations of DWD phenology held out from a separate year (2021) and those derived from satellite data reveal that it typically ranged within ±10 days for almost all phenological phases. The validation results of the detection of dates of phenological phases against separate field-based phenological observations resulted in an RMSE of less than 10 days and an R-squared value of approximately 0.9 or greater. The findings demonstrate how optimising the thresholds required for deriving crop-specific phenophases using high-quality NDVI time series data could produce timely and spatially explicit phenological information at the field and crop levels.

Integration of bare soil and crop growth remote sensing data to improve the accuracy of soil organic matter mapping in black soil areas

Accurately mapping the spatial distribution of soil organic matter (SOM) content is critical for informed land management decisions and comprehensive climate change analyses. Remote sensing-based SOM mapping models during periods of bare soil exposure have demonstrated efficacy in various regional studies. However, integrating bare soil imagery with growing season imagery for SOM content mapping remains a complex process. We conducted a study in Youyi Farm, a representative area of black soil in Northeast China. We collected 574 soil samples (0–20 cm) with SOM content through field sampling and laboratory analysis. Additionally, cloud-free Sentinel-2 images were obtained from the Google Earth Engine (GEE) platform for both the bare soil period (April-June, October) and crop growth period (July-September) from 2019 to 2021. To assess the influence of crop growth information on SOM mapping, we incorporated remote sensing imagery during the crop growth period, considering different crop type zones (maize (Zea mays L.), soybean (Glycine max L.), and rice (Oryza sativa L.)). We conducted overall and zonal regressions using the random forest (RF) model to validate the prediction results through cross-validation. Our findings indicate that: (1) adding crop growth period images to the bare soil period images in different years can improve the accuracy of SOM mapping. For example, in the overall regression model of 2020, the highest accuracy was achieved by using the combination of May-July images, with an R2 value of 0.70 and an RMSE value of 0.71 %; (2) zonal regression by differentiating crop types can effectively improve the SOM mapping accuracy. In 2019, using zonal regression, the R2 of SOM mapping accuracy was improved by 0.02 and the RMSE was reduced by 0.03 % compared with the overall regression; (3) precipitation is an important factor affecting the accuracy of SOM prediction, and the lower the precipitation, the higher the accuracy of SOM prediction. In summary, the results of this study show that in the SOM remote sensing mapping of the black soil area, the growing period remote sensing information of different crop types should be comprehensively considered and combined with the image data of the years of lower precipitation, the accuracy of the SOM mapping can be effectively improved, which provides a new technological path and an application basis for the enhancement of the accuracy in remote sensing mapping with soil attributes.

Mechanized No-Tillage Planting with Maize Straw Mulching Improves Potato Yield and Water Use Efficiency in Arid Regions of Northwest China

To explore the yield-increasing mechanism of mechanized potato planting with corn straw mulching, a two-year (2021 and 2022) field experiment was conducted to study the effects of mechanized no-tillage with straw mulching on potato yield and water use efficiency. This experiment included mechanized no-tillage potato planting with corn straw mulch covering (JG), plastic film mulching (HM), and open flat planting (CK). The results showed that mechanical no-tillage with straw mulching significantly affected soil water content in the 0–100 cm soil layer, yield, and water use efficiency (p < 0.05). There was no significant difference in yield between JG and HM, but it was significantly higher than that of CK. The yield of JG was 3.09~12.27% higher than that of CK. The yield increase was mainly achieved by increasing the potato weight per plant (0.697~0.862 **) and the average single potato weight (0.048~0.631). The tuber weight per plant was positively correlated with the plant height at the seedling stage (0.03~0.92 **) and positively correlated with the dry weight of stems and leaves at the tuber expansion stage and starch accumulation stage (0.74 **~0.95 **). It was negatively correlated with the number of branches at the tuber formation stage (−0.33~−0.88 **). Compared with CK, JG could significantly improve the water use efficiency of potatoes and reduce water consumption during the whole growth period of crops. In 2021, JG was 6.5% higher than CK, and HM was 6.88% lower than CK. In 2022, JG and HM increased water use efficiency by 26.17% and 14.50% compared with CK. When HM is applied in heavy soil areas, soil compaction can easily occur, which affects seedling emergence and reduces yield. At the same time, JG has strong adaptability to soil types and good yield stability. It can be seen that JG is a green and efficient mechanized potato cultivation technology suitable for dry farming areas.

Quantitative inversion of soil trace elements from spectroscopic effects across multiple crop growth periods

Human health is directly impacted by trace components found in soil, for which hyperspectral remote sensing technology offers a novel way to rapidly assess dynamic soil environmental quality. In most studies, the premise of quantitative inversion on soil elements is that the known target elements will cause growth stress. However, such stress is unusual in non-polluted areas. Consequently, broad-area soil trace element monitoring in non-contaminated areas remains challenging. Spectral inversion of plant material has a longer time window and is more sensitive to elemental changes than soil spectral inversion (bare soil period). In this work, we conducted a wheat pot experiment with concentration gradients of four trace elements (Fe, B, Mo, and Zn), analyzed leaf (jointing stage) and spike (maturity period) samples using spectral measurements and chemical tests, and filtered the characteristic positions and inverse modeling using the spectra of the element-accumulating preference organs in plant canopies. The canopy aggregation sites differed for each element, and both simple linear regression (SLR) and multiple linear regression (MLR) models based on the spectra of canopy aggregation sites achieved high accuracy. The results of this study enable the construction of an inverse model of plant spectra-plant element content-elements in soil, which can serve as a reference for soil monitoring and assessment in typical crop cover areas.

A Dynamic Process-Based Method for Screening Salt-Tolerant and High-Yielding Crop Varieties

Maize, the most important cereal crop worldwide, is moderately sensitive to salt stress. Given the challenges of soil salinization, developing methods to screen and cultivate salt-tolerant maize varieties is vital for enhancing food security. Among the options, process model-based assisted screening is an effective method for faster and more thorough screening of salt-tolerant varieties. In this study, a method for quickly screening salt-tolerant and high-yielding crop varieties is proposed by combining a coupled model of crop growth and water–salt response with fitness function. Then, this method was applied to the saline areas of the Hetao Plain in China to demonstrate its applicability. This study includes three parts. Firstly, field trial data from 10 commonly grown maize varieties in Hetao Plain (i.e., Xianyu 335, Yinyu 238, Jindan 73, Deke 622, DK-815, Heyu 157, Xianyu 1321, Jinrun 919, Tianci 19, and Xianyu 1225) were used to calibrate the model for different maize varieties. Moreover, model accuracy was evaluated using four indexes including the regression coefficient (b), coefficient of determination (R²), root mean square error (RMSE), and Nash–Sutcliffe efficiency coefficient (NSE). Secondly, scenario simulations were conducted using the calibrated model by setting nine initial salinity scenarios to simulate daily dynamic crop growth and soil water–salt changes for the 10 maize varieties. Finally, salt-tolerant and high-yielding maize varieties were screened based on the fitness function method during crop growth periods. The results showed that the simulation model was applicable and functioned effectively for all 10 maize varieties in the region, with the determination coefficient (R2) and Nash–Sutcliffe efficiency coefficient (NSE) of simulated plant height and leaf area index being above 0.90. Furthermore, the R2 of soil water content, soil electrical conductivity, and groundwater depth are 0.51, 0.52, and 0.60, respectively. Afterward, the fitness function values were calculated to bridge the linkage between simulated indicators and scenarios to screen varieties step by step according to the predetermined percentage of screening. Jindan 73, Xianyu 1225, and DK-815 were eventually determined as the most suitable salt-tolerant and high-yielding maize varieties. Therefore, the above results show that the proposed method is suitable for saline crop variety screening with flexibility and applicability.

The impact of intra-annual temperature fluctuations on agricultural temperature extreme events and attribution analysis in mainland China

Daily temperature variations, which tend to exhibit non-constant and non-linear patterns, are often characterized by intra-annual fluctuations that cause severe and frequent extreme temperature events that have an enormous impact on agricultural production. However, the quantitative relationship between intra-annual temperature fluctuations and extreme agricultural temperatures remains unclear. We aimed to investigate intra-annual temperature fluctuation changes based on daily meteorological data in nine agricultural regions across China from 1960 to 2022 and quantify the impact of temperature fluctuations on extreme agricultural temperatures during crop growth periods. Moreover, an attribution analysis of intra-annual temperature fluctuations was performed using climate indicators. Main results showed: (1) intra-annual temperature fluctuations in each region exhibited a certain decrease, and the spatial distribution showed a significant decreasing trend from north to south. (2) Intra-annual temperature fluctuations have moderately exacerbated extreme agricultural hot and cold events during growth periods, which have brought serious challenges to agriculture owing to advances in phenology and unsynchronized rain heat compared to climate warming. The proportion of positive correlations between temperature fluctuations and extreme temperatures was much larger than that of the negative correlations in nearly all of China (percentage of stations: 20.5 %), and the negative correlation was concentrated only in southern China (percentage of stations: 3.7 %). (3) Hydrothermal coupling and elevation moderately affected intra-annual temperature fluctuations. Average temperature, relative humidity, and elevation had negative correlations with intra-annual temperature fluctuations, the correlation coefficients (R) were − 0.62, −0.42 and − 0.12, respectively; however, reference crop evapotranspiration (ET0) exhibited a positive correlation (R: 0.29), and all reached highly significant levels (P < 0.01). Climate indicators mainly affected intra-annual temperature fluctuations in eastern China. Specifically, the El Niño-Southern Oscillation (ENSO), unstable Asia Polar Vortex, and increased Western Pacific Subtropical High have enhanced temperature fluctuations. The deepened East Asian Trough and Arctic Oscillation of the negative phase weakened the intra-annual temperature fluctuations. This investigation highlights the crucial function of temperature fluctuation in intensifying extreme temperature occurrences and provides a more reasonable scientific foundation for extreme event prediction and agricultural planning.

Decoding stomatal characteristics regulating water use efficiency at leaf and plant scales in rice genotypes.

Stomatal traits in rice genotypes affect water use efficiency. Low-frequency small-size stomata correlate with whole plant efficiency, while low-frequency large-size stomata show intrinsic efficiency and responsiveness to vapour pressure deficit. Leaf surface and the patterning of the epidermal layer play a vital role in determining plant growth. While the surface helps in determining radiation interception, epidermal pattern of stomatal factors strongly regulate gas exchange and water use efficiency (WUE). This study focuses on identifying distinct stomatal traits among rice genotypes to comprehend their influence on WUE. Stomatal frequency ranged from 353 to 687 per mm2 and the size varied between 128.31 and 339.01 μm2 among 150 rice germplasm with significant variability in abaxial and adaxial surfaces. The cumulative water transpired and WUE determined at the outdoor phenomics platform, over the entire crop growth period as well as during specific hours of a 24h-day did not correlate with stomatal frequency nor size. However, genotypes with low-frequency and large-size stomata recorded higher intrinsic water use efficiency (67.04μmol CO2 mol-1 H2O) and showed a quicker response to varying vapour pressure deficit that diurnally ranged between 0.03 and 2.17kPa. The study demonstrated the role of stomatal factors in determining physiological subcomponents of WUE both at single leaf and whole plant levels. Differential expression patterns of stomatal regulatory genes among the contrasting groups explained variations in the epidermal patterning. Increased expression of ERECTA, TMM and YODA genes appear to contribute to decreased stomatal frequency in low stomatal frequency genotypes. These findings underscore the significance of stomatal traits in breeding programs and strongly support the importance of these genes that govern variability in stomatal architecture in future crop improvement programs.

Effect of nano-urea and irrigation regimes on growth parameters of wheat (Triticum aestivum L.)

Wheat (Triticum aestivum L.) is a crucial cereal crop globally, serving as a staple food for a vast population. Its productivity faces challenges such as nutrient imbalance and terminal heat stress exacerbated by climate change, particularly in regions like the North-Eastern Plain Zone of India. To address these issues, a study was conducted during the rabi season of 2021–2022 in Jharkhand, India, aiming to examine the impacts of irrigation regimes and nano-urea-based nitrogen management practices on wheat. Employing a split-plot design with three replications, the experiment assessed three irrigation regimes and five nitrogen management practices. Results revealed that irrigation regimes registered non-significant effects on crop growth parameters like plant height, LAI (leaf area index), root dry weight, flag leaf area and tillers/m2 , which might be due to frequent rain up to the flowering stage during the crop growth period. Higher levels of nitrogen in commercial urea significantly increased plant height, LAI, flag leaf area, recovered root dry weight and dry matter accumulation. However, nano-urea spray at 45 DAS helped crop plants positively to increase all growth parameters more compared to nano-urea spray at 60 DAS. All growth attributes with 50% RDN+2-nano-urea spray were statistically at par with 50% RDN+1 nano-urea spray up to 40 DAS, but after the flowering stage, it was statistically similar with 100% RDN. The maximum tillers/m2 was found under 100% RDN, followed by 75% RDN+ one nano-urea spray and 50% RDN+2 nano-urea spray. Thus, 3- irrigations at CRI, flowering and milk stage and 100% RDN (120 kg N/ha) were found sufficient for enhancing the growth parameters of wheat.

Evaluating annual soil carbon emissions under biochar-added farmland subjecting from freeze-thaw cycle

Straw biochar is a commonly recognized agricultural amendment that can improve soil quality and reduce carbon emissions while sequestering soil carbon. However, the mechanisms underlying biochar's effects on annual soil carbon emissions in seasonally frozen soil areas and intrinsic drivers have not been clarified. Here, a 2-y field experiment was conducted to investigate the effects of different biochar dosages (0, 15, and 30, t ha−1; B0 (CK), B15, and B30, respectively) on carbon emissions (CO2 and CH4) microbial colony count, and soil-environment factors. The study period was the full annual cycle, including the freeze-thaw period (FTP) and the crop growth period (CP). Structural equation modeling (SEM) was developed to reveal the key drivers and potential mechanisms of biochar on carbon emissions. Biochar application reduced soil carbon emissions, with the reduction rate positively related to the biochar application rate (B30 best). During FTP, the reduction rate was 11.5% for CO2 and 48.2% for CH4. During CP, the reduction rate was 17.9% for CO2 and 34.5% for CH4. Overall, compared with CK, B30 treatment had a significant effect on reducing total soil carbon emissions (P < 0.05), with an average decrease of 16.7% during the two-year test period. The study also showed that for soils with continuous annual cycles (FTP and CP), carbon emissions were best observed from 10:00–13:00. After two years of freeze-thaw cycling, biochar continued to improve soil physical and chemical properties, thereby increasing soil microbial colony count. Compared with B0, the B30 treatment significantly increased the total colony count by 74.3% and 263.8% during FTP and CP (P < 0.05). Structural equation modeling (SEM) indicated that, with or without biochar application, the soil physicochemical properties directly or indirectly affected soil CO2 and CH4 emission fluxes through microbial colony count. The total effects of biochar application on CO2 emission fluxes were 0.50 (P < 0.05) and 0.64 (P < 0.01), respectively, but there was no significant effect on CH4 emission fluxes (P > 0.05). Among them, soil water content (SWC), soil temperature (ST) and soil organic carbon (SOC) were the main environmental determinants of CO2 emission fluxes during the FTP and CP. The total effects were 0.57, 0.65, and 0.53, respectively. For CH4, SWC, soil salinity (SS) and actinomycete colony count were the main environmental factors affecting its emission. The total effects were 0.50, 0.45, 0.44, respectively. For freeze-thaw alternating soils, the application of biochar is a feasible option for addressing climate change through soil carbon sequestration and greenhouse gas emissions mitigation. Soil water-heat-salt-fertilization and microbial communities are important for soil carbon emissions as the reaction matrix and main participants of soil carbon and nitrogen biochemical transformation.

Enhancing crop productivity in photovoltaic greenhouses using extended PAR-based photosynthesis

With the global population expected to reach 11 billion by the end of this century, the imperative of enhancing crop productivity per unit of land while conserving resources is paramount. This paper presents a novel approach to achieving these goals through Extended PAR-based photosynthesis (EPBP) methodology, specifically tailored for standalone Photovoltaic Greenhouses (PVGHs). By leveraging LED technology and extending photosynthesis during dusk and dawn periods, this methodology aims to enhance crop yield without substantial additional infrastructure investment. A comprehensive real-field experiment, conducted over a complete tomato crop cycle of 96 days, demonstrates a significant increase in crop yield, with a 51.29 % improvement compared to conventional PVGH without additional photosynthesis. Through meticulous analysis of key performance indicators such as leaf area index, specific leaf area, and dry matter weight, this study substantiates the efficacy of EPBP in optimizing crop growth within PVGHs. Further, the ROI of 14.11 % and 12.54 % have been observed for tomato crop grown with and without EPBP under the PVGH environment. It not only reduces crop growth period but also enhances both quality and yield quantity without additional operational expenses. The scope of the presented work is limited to the quantitative measurement; however, its impact on the quality of the crop yield could be further explored.

Functionality of arbuscular mycorrhizal fungi varies across different growth stages of maize under drought conditions

Physio-biochemical regulations governing crop growth period are pivotal for drought adaptation. Yet, the extent to which functionality of arbuscular mycorrhizal fungi (AM fungi) varies across different stages of maize growth under drought conditions remains uncertain. Therefore, periodic functionality of two different AM fungi i.e., Rhizophagus irregularis SUN16 and Glomus monosporum WUM11 were assessed at jointing, silking, and pre-harvest stages of maize subjected to different soil moisture gradients i.e., well-watered (80% SMC (soil moisture contents)), moderate drought (60% SMC), and severe drought (40% SMC). The study found that AM fungi significantly (p < 0.05) affected various morpho-physiological and biochemical parameters at different growth stages of maize under drought. As the plants matured, AM fungi enhanced root colonization, glomalin contents, and microbial biomass, leading to increased nutrient uptake and antioxidant activity. This boosted AM fungal activity ultimately improved photosynthetic efficiency, evident in increased photosynthetic pigments and photosynthesis. Notably, R. irregularis and G. monosporum improved water use efficiency and mycorrhizal dependency at critical growth stages like silking and pre-harvest, indicating their potential for drought resilience to stabilize yield. The principal component analysis highlighted distinct plant responses to drought across growth stages and AM fungi, emphasizing the importance of early-stage sensitivity. These findings underscore the potential of incorporating AM fungi into agricultural management practices to enhance physiological and biochemical responses, ultimately improving drought tolerance and yield in dryland maize cultivation.

Quantitative evaluation of soil water balance under a ridge-furrow rainwater harvesting system in Chinese rainfed agroecosystem.

The ridge-furrow rainwater harvesting system (RFRH) is an advanced farmland management technology that plays a vital role in making full use of rainwater resources. However, it is not clear that RFRH affects crop yield and water use efficiency (WUE) by regulating soil water storage (SWS). Therefore, the present study conducted a meta-analysis to make a large compilation of previous studies and indirectly quantify the impact of RFRH on crop yield and WUE by analysing the effect of RFRH on SWS. The results showed that RFRH improved crop yield and WUE by 26.71% and 25.86%, respectively, by increasing SWS by 3.93% compared to the traditional flat cultivation. RFRH had a significant effect on increasing crop yield and WUE and improving SWS. A low ridge-furrow ratio and ridge-furrow mulching were recommended to obtain positive effects on crop yield and WUE when potatoes are grown in areas with high precipitation (600-800 mm). Furthermore, when nitrogen fertilization is applied during the crop growth period, we also found that a medium nitrogen fertilizer rate is recommended to achieve a significant positive effect on crop yield and WUE. Importantly, a win-win analysis showed the proportions of various groups in the target zone (zone I) to determine the appropriate strategy for RFRH of crops. The present study provides a scientific reference for the future application of the RFRH. The study provides scientific recommendations on crop types, ridge-furrow configurations, plastic mulching patterns and nitrogen fertilizer rate for future RFRH applications. © 2024 Society of Chemical Industry.