Crop Growth Processes Research Articles

Modelling water/heat transfer and crop growth under film mulching condition in a seed–maize field

Film mulching has been widely applied in arid and semi–arid areas around the world. Current models simulating the process of water, heat, and crop growth generally neglect or simplify the role of film mulching in energy balance and water/heat exchange. In this study, CropSMPAC model was established based on previously developed model (CropSPAC, the soil water/heat transfer and crop growth model), by incorporating the influence of film mulching on energy budget and partition, sensible and latent heat fluxes of the soil surface, precipitation interception, and crop growth. The CropSMPAC model was calibrated and validated using experimental data from a seed–maize field located in arid Northwest China for the years 2017, 2018, and 2019. The experimental setup included two mulching treatments, i.e., full film mulching (M1) and no mulching (M0) along with three irrigation treatments, i.e., full irrigation (WF), medium irrigation (WM, 70%WF), low irrigation (WL, 40%WF). The results demonstrated that the model performed well in simulating the dynamics of field water, heat, and crop growth under various film mulching and irrigation conditions. The Nash-Sutcliffe efficiency coefficients for net radiation, soil heat flux, soil water storage (SWS), soil temperature (ST), leaf area index (LAI), aboveground dry biomass (ADB) were 0.57, 0.37, 0.79, 0.76, 0.95, 0.95, respectively. The relative errors for seed-maize yield, crop evapotranspiration (ET) were 9.38%, 9.10%, respectively. Furthermore, the model accurately reflected the differences in SWS and ST due to different mulching conditions (M1 and M0). The observed differences in SWS between M1 and M0 ranged from –30.6 to 32.9 mm, while the simulated differences ranged from –30.5 to 30.2 mm under various irrigation treatments. Similarly, the observed differences in ST between M1 and M0 were 1.8, 1.7, 1.0 and 1.4 °C at 10, 20, 40 and 80 cm soil depths, respectively, while the simulated differences were 2.3, 2.1, 1.7 and 0.8 °C under WF treatment. Additionally, the model effectively simulated the effects of film mulching on advancing the crop growth period and promoting LAI, ADB and yield. Overall, the model accurately captured the changes in energy fluxes, soil water, heat and crop growth caused by plastic film mulching, demonstrating that it is a useful tool for irrigation water management and crop production in crop land with film mulching.

Carbon nanomaterials are a superior soil amendment for sandy soils than biochar based on impacts on lettuce growth, physiology and soil biochemical quality

A significant bottleneck of current agricultural systems remains the very low agronomic efficiency of conventional agrochemicals, particularly in sandy soils. Carbon nanomaterials (CNMs) have been proposed to address this inefficiency in sandy soils, which could potentially improve soil fertility and enhance crop growth and physiological processes. However, the effects of different rates of CNMs on crop physiological and soil biochemical quality in sandy soils must be compared to other carbon sources (e.g., biochar) before CNMs can be broadly used. To address this, a 70-day pot experiment was set up, growing lettuce under ten treatments: a negative control with no CNMs, biochar or fertilizer; a fertilizer-only control; three CNMs-only unfertilized treatments (CNMs at 200, 400 and 800 mg kg−1 soil); two biochar treatments with fertilizer (biochar at 0.5% and 1% by soil mass + fertilizer); and three CNMs treatments with fertilizer (CNMs at 200, 400 and 800 mg kg−1 soil + fertilizer). A novel amorphous, water-dispersible, and carboxyl-functionalized CNMs with pH of 5.5, zeta potential of −40.6 mV and primary particle diameter of 30–60 nm was used for this experiment. Compared to the fertilizer-only control, CNMs applied at low to medium levels (200–400 mg kg−1) significantly increased lettuce shoot biomass (20–21%), total chlorophyll (23–27%), and fluorescence and photosynthetic activities (4–10%), which was associated with greater soil nutrient availability (N: 24–58%, K: 68–111%) and higher leaf tissue accumulation (N: 25–27%; K: 66%). Low to medium levels of CNMs also significantly increased soil biochemical properties, such as higher soil microbial biomass carbon (27–29%) and urease enzyme activity (34–44%) relative to fertilizer-only applications. In contrast, biochar (0.5%) increased lettuce biomass relative to fertilizer-only but had no significant effect on soil fertility and biological properties. These results suggest that CNMs at low to medium application rates are a superior carbon-based amendment relative to biochar in sandy soils.

Design an Integrated Web-Based Plant Commodity Monitoring Information System

In order to increase agricultural commodities in the Jombang Regency area to be higher, innovations are needed that can have a positive impact on the progress of the existing agricultural sector. One aspect that can support this progress is the existence of a plant commodity monitoring information system. The crop commodity monitoring system is a web-based application aimed at viewing and assessing the process of food crop growth systematically, starting from the planting period to the harvest period. From this system , historical crop data will be obtained, where these data can then be used as knowledge material for the Jombang Regency Agriculture Office in terms of policy making, such as the supply of seeds and fertilizer needs on subsidized and non-subsidized land, determination of planting periods, or predictions of agricultural products next year. In addition, this system also provides information related to plant conditions, plant age and instructions in determining plants to suit existing land. To realize this, the author designed and built an integrated web-based crop commodity monitoring information system on agricultural land. This research uses R&D (Research and Development) method. The test results using Black Box show that the functionality of this information system has been running well. The final result obtained from this study is that the system is more efficient and has comprehensive data than the old system that has been operating in the Jombang Regency Agriculture Office. Keywords: Agriculture, Plant Commodities, Monitoring, Web, Jombang Regency Agriculture Office

PEAS AS A FACTOR INCREASING THE PRODUCTIVITY OF AGROPHYTOCENOSES

The article presents the results of studies of the productivity of annual crops agrophytocenoses with peas depending on the use of different doses of mineral fertilizers and pre-sowing seed treatment with microelements. According to the biological characteristics of growth and development, the cereal components of grass mixtures differ in the duration of the growing season. Thus, the phase of milky ripeness of spring barley occurs 8-10 days earlier than that of spring triticale and 18-20 days earlier than that of oats. It was found that the created two- and three-component agrophytocenoses consisting of spring triticale, peas and white mustard were 19.5-27.6 % higher in green mass yield compared to spring barley in a mixture with peas and white mustard. The yield of dry matter was 8.42-8.93 and 1.132-1.189 t/ha of crude protein, which was 1.6-1.7 times higher than in the agrophytocenosis with spring barley as the dominant crop. The effectiveness of nitrogen fertilizers at a dose of 60 kg/ha of active ingredient, which provide an increase in the height of field pea plants - by 14.7 and 12.1% of fodder oats, compared to the application of 30 kg/ha on the background of phosphorus-potassium fertilizer (P30K30), was established. The treatment of seeds of both components of agrophytocenosis with microelements Zn and Mo, which was carried out before sowing, stimulated the growth processes of crops. Thus, the height of plants in fodder oats increased by 3.4-6.7 cm and in field peas - by 4.9-5.5 cm on the mineral background of nutrition. The positive effect of mineral fertilisers in a dose of N60P30K30 and seed treatment with trace elements Zn and Mo on the yield of nutrients in the agrophytocenosis was proved. A binary mixture of field peas with a seeding rate of 40% of the full seeding rate and fodder oats of 60% provided the highest dry matter yield of 10.3 and 2.24 t/ha of crude protein. It can be argued about the agrobiological feasibility of growing fodder oats and spring triticale with high-protein crops, which provide a conveyor supply of plant raw materials for harvesting various types of feed and the efficiency of using agrometeorological resources during the period of harvest formation.

A new regional cotton growth model based on reference crop evapotranspiration for predicting growth processes

Meteorological conditions and irrigation amounts are key factors that affect crop growth processes. Typically, crop growth and development are modeled as a function of time or growing degree days (GDD). Although the most important component of GDD is temperature, it can vary significantly year to year while also gradually shifting due to climate changes. However, cotton is highly sensitive to various meteorological factors, and reference crop evapotranspiration (ETO) integrates the primary meteorological factors responsible for global dryland extension and aridity changes. This paper constructs a cotton growth model using ETO, which improves the accuracy of crop growth simulation. Two cotton growth models based on the logistic model established using GDD or ETO as independent factors are evaluated in this paper. Additionally, this paper examines mathematical models that relate irrigation amount and irrigation water utilization efficiency (IWUE) to the maximum leaf area index (LAImax) and cotton yield, revealing some key findings. First, the model using cumulative reference crop evapotranspiration (CETO) as the independent variable is more accurate than the one using cumulative growing degree days. To better reflect the effects of meteorological conditions on cotton growth, this paper recommends using CETO as the independent variable to establish cotton growth models. Secondly, the maximum cotton yield is 7171.7 kg/ha when LAImax is 6.043 cm2/cm2, the corresponding required irrigation amount is 518.793 mm, and IWUE is 21.153 kg/(ha·mm). Future studies should consider multiple associated meteorological factors and use ETO crop growth models to simulate and predict crop growth and yield.

Research on Winter Wheat Growth Stages Recognition Based on Mobile Edge Computing

The application of deep learning (DL) technology to the identification of crop growth processes will become the trend of smart agriculture. However, using DL to identify wheat growth stages on mobile devices requires high battery energy consumption, significantly reducing the device’s operating time. However, implementing a DL framework on a remote server may result in low-quality service and delays in the wireless network. Thus, the DL method should be suitable for detecting wheat growth stages and implementable on mobile devices. A lightweight DL-based wheat growth stage detection model with low computational complexity and a computing time delay is proposed; aiming at the shortcomings of high energy consumption and a long computing time, a wheat growth period recognition model and dynamic migration algorithm based on deep reinforcement learning is proposed. The experimental results show that the proposed dynamic migration algorithm has 128.4% lower energy consumption and 121.2% higher efficiency than the local implementation at a wireless network data transmission rate of 0–8 MB/s.

A novel water use efficiency model based on stomata coupling crop growth and farmland water cycle processes in arid area

Water use efficiency (WUE) determined by water-carbon coupling process is important to evaluate the relationship between crop growth and water consumption. Aiming to provide a novel WUE dynamic evaluating method at field scale, we developed a crop WUE model based on stomata, which coupled the processes of crop growth and farmland water cycle. In previous work, we developed a stomatal conductance model in which crop stomatal conductance could be described by its relationship to meteorological factors, soil water and salinity. In this study, we coupled crop transpiration and photosynthesis based on stomata. Furthermore, the model described the daily crop growth and hydrological processes. As a result, the developed model could simulate the dynamic changes of crop canopy evapotranspiration (ET), canopy photosynthesis, dry matter accumulation and assimilation distribution, soil water movement, soil salt transport and WUE of farmland ecosystems on a daily scale. Three years field experimental data were used to calibrate the developed model. The results showed that the model could simulate the variation of agricultural hydrology processes properly in sunflower field on the daily scale, with determination coefficient (R2) and Nash-Sutcliffe efficiency coefficient (NSE) of ET, transpiration and WUE all close to 0.60. R2 and NSE of net ecosystem productivity above 0.74 and 0.68, respectively. And the coupling model had a good performance in simulating the dynamic change process of soil moisture and salt with the R2 and NSE above 0.71 and 0.61, respectively. Sensitivity analysis of model parameters showed that the selection of the extinction coefficient had a great influence on the accuracy of the model and the accurate description of soil resistance parameters played a crucial role in the accurate quantification of ET. The developed field WUE model can provide novel method to evaluate daily hydrological processes, agricultural productivity and WUE dynamics.

Prediction of heading date, culm length, and biomass from canopy-height-related parameters derived from time-series UAV observations of rice.

Unmanned aerial vehicles (UAVs) are powerful tools for monitoring crops for high-throughput phenotyping. Time-series aerial photography of fields can record the whole process of crop growth. Canopy height (CH), which is vertical plant growth, has been used as an indicator for the evaluation of lodging tolerance and the prediction of biomass and yield. However, there have been few attempts to use UAV-derived time-series CH data for field testing of crop lines. Here we provide a novel framework for trait prediction using CH data in rice. We generated UAV-based digital surface models of crops to extract CH data of 30 Japanese rice cultivars in 2019, 2020, and 2021. CH-related parameters were calculated in a non-linear time-series model as an S-shaped plant growth curve. The maximum saturation CH value was the most important predictor for culm length. The time point at the maximum CH contributed to the prediction of days to heading, and was able to predict stem and leaf weight and aboveground weight, possibly reflecting the association of biomass with duration of vegetative growth. These results indicate that the CH-related parameters acquired by UAV can be useful as predictors of traits typically measured by hand.

Retrieval of crop biophysical-biochemical variables from airborne AVIRIS-NG data using hybrid inversion of PROSAIL-D

Sustainable agricultural growth and management reduces over-utilization of farm resources and squeezes risk of negative impacts on environment. Monitoring continuous crop growth and health under various conditions at different spatio-temporal resolutions is a key to assess yield stability, crop diversity, adaptability, mitigation for stress and response. The quantification of crop biophysical and biochemical variables spatially from remotely sensed data with help of spectroscopic methods provide a reliable discerning information in the context of crop foliar condition like leaf greenness, senescence, canopy density, crop growth, stress and eco-physiological processes. Airborne Visible/Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) airborne hyperspectral data offers high spatial and spectral resolution giving a unique advantage and opportunity to test retrievals of crop biophysical (BP)-biochemical (BC) variables under varying conditions over different types of crops. A hybrid inversion of leaf-canopy Radiative Transfer model – PROSAIL-D complemented with use of data driven nonlinear non-parametric methods which offer simplicity, fastness, reliability and competency is a powerful method to retrieve crop biophysical and biochemical variables. The Hyperspectral band (feature) selection is a computationally cost efficient method to overcome data redundancy in high dimensional correlated input spectral bands. The determination of optimum subset or combination of hyperspectral bands specific to retrieval of vegetation properties (including canopy effects) determined using feature selection algorithm for regression-based retrievals are active research topic unlike classification problems in which it is more common. A band selection algorithm based on Gaussian Processes Regression was used to choose most sensitive bands from in-situ biophysical-biochemical measurements and crop spectral signatures collected for analysis in two different agricultural regions: Raichur (Karnataka) and Anand (Gujarat) districts of India representing diverse landscapes, heterogeneous crop canopies and agro climatic settings. The retrieval algorithm for AVIRIS-NG image employed a decision tree ensemble algorithm Canonical Correlation Forests using the optimum subset of bands for retrieval of targeted crop variable. Validation of retrieved crop variables were done using in-situ ground observations collected over heterogeneous diverse crop landscape. The results showed chlorophyll (RMSE = 6.61 µg cm−2), equivalent water thickness (RMSE = 0.002 cm), leaf area index (RMSE = 0.35 m2/m−2) and dry matter (RMSE = 0.003 g cm−2), carotenoid (RMSE = 14.3 µg cm−2), anthocyanin (RMSE = 12.92 µg cm−2) retrieved with better accuracies. The results obtained in context of feature (band) selection approach for Radiative Transfer model inversion show overlapping of sensitive spectral bands of chlorophyll-ab, carotenoid and anthocyanin especially between narrow spectral range 480 to 560 nm as predominant reason for decreased accuracies of anthocyanin and carotenoid compared to other variable. This poses a limitation as well as opportunity for further research in signal separation in context of feature selection approach especially in context of broader spectral bandwidths. It also sets ground for further development of feature selection algorithms that use hybrid regression methods customized for crop specific traits.

KSTAGE: A knowledge-guided spatial-temporal attention graph learning network for crop yield prediction

Accurate and timely crop yield prediction is difficult to achieve due to the nonlinear and dynamic spatial–temporal correlations included during the crop growth process. The latest approaches utilize deep learning techniques to implicitly capture sequential dependencies but fail to consider prior knowledge. In this paper, a novel Knowledge-guided Spatial–Temporal Attention Graph Network (KSTAGE), which incorporates prior knowledge to model dynamic temporal relationships and geospatial topology structures, is proposed. The proposed KSTAGE first utilizes a 3D convolutional neural network (CNN) to embed the initial spectral feature. Following its development, a new Knowledge-guided Temporal Multi-head Attention Algorithm (KTMA), the core of KSTAGE, is applied to automatically generate temporal attention weights from a self-attention mechanism under the guidance of prior knowledge. Furthermore, a new strategy for aligning self-attention scores with the prior distribution is developed. Finally, a location-aware Spatial Attention Graph Network utilizing geospatial knowledge is proposed to aggregate the spatial neighborhood features for the final yield prediction. The experimental results obtained on county-level yield prediction tasks in China and the contiguous United States (CONUS) demonstrate that the proposed KSTAGE achieves obvious improvements over the baselines.

The distributed CERES-Maize model with crop parameters determined through data assimilation assists in regional irrigation schedule optimization

Crop model–based irrigation scheduling is essential for improving crop production and water resource allocation. However, the parametrization of crop growth processes, for which relevant genetic coefficients must be determined through field scale experiments, is usually hampered by poor spatial representation; this limitation compromises the robustness of irrigation scheduling at the regional scale. We propose a framework for optimizing regional irrigation schedules; this framework involves using a distributed crop model with crop parameters determined through data assimilation. We first conducted a sensitivity analysis to identify the key genetic coefficients (P2: photoperiod sensitivity coefficient; P5: thermal time from silking to physiological maturity; G3: kernel filling rate during the linear grain filling stage and under optimal conditions) affecting the leaf area index (LAI) in the CERES-Maize model. Subsequently, the field-scale CERES-Maize model was calibrated and validated using data collected from field experiments conducted in 2016 and 2017, respectively. Subsequently, the LAI estimated from 2020 microwave backscattering data was used to assimilate remote sensing information and crop model in order to obtain the key genetic coefficients of maize in the distributed crop model. Finally, on the basis of the distributed crop model, a multiobjective genetic algorithm was executed to optimize irrigation schedules under various meteorological scenarios (e.g., precipitation and reference evapotranspiration). The LAI of maize at a regional scale was accurately estimated using backscattering information extracted from microwave remote sensing images (R2 = 0.94, RMSE = 0.27). Compared with the crop model parameters calibrated at the field scale, the crop model parameters determined through data assimilation resulted in more accurate simulations of LAI, soil water content, and yield at the regional scale. The irrigation schedule based on the distributed model increased the maize yield corresponding to the local current irrigation schedule by 125–851 kg/ha, increased the water use efficiency by 0.02–0.17 kg/m3, and reduced the irrigation amount by 2–60 mm. The proposed framework for estimating genetic coefficients based on data assimilation methods helps in regional irrigation scheduling for other crops.

Regional modeling of winter wheat yield and water productivity under water-saving irrigation scenarios

Abstract Wheat is the major agricultural crop in Iran. Using reliable tools to estimate wheat grain yield may help the regional planners and stakeholders to employ proper cultivation approaches to increase grain yield in different regions. The process-based crop growth models are the essential analytical tools for representing the main interactions between the environment, crop, resources, and yield production. For these reasons, the crop growth models have been used as an important component in productive farming systems for assessing and improving the crop production at field and regional scales. In this study, the parameterized AquaCrop model was employed to simulate the four major elements that affect the crop growth process, such as soil water content, canopy cover, biomass, and grain yield, of three irrigated winter wheat cultivars. The field experiments were conducted in four locations with different climatic conditions in the south and southwest of Iran during three consecutive growing seasons of 2016–2017, 2017–2018, and 2018–2019. Results showed that AquaCrop had reliable simulation of soil water content with normalized root mean squared error (NRMSE) and Nash–Sutcliffe coefficient (CNS) ranging from 0.05 to 0.15 and 0.69 to 0.85, respectively, for the four study locations. The NRMSE and CNS ranged from 0.10 to 0.21 and 0.83 to 0.97 for canopy cover, respectively. The NRMSE and CNS values were 0.11–0.26 and 0.80–0.97 for biomass, respectively. These results indicate that the parameterized AquaCrop model has high skill in the simulation of soil water content, canopy cover, biomass, and grain yield of different winter wheat cultivars in the vast regions in the southern part of Iran. The model was then used to simulate wheat grain yield and irrigation water productivity (WP) for 324 scenarios, including nine sowing dates, three irrigation levels, and three climatic conditions. AquaCrop showed that it was skillful to simulate grain yield and WP of different irrigated winter wheat cultivars in different regional climatic conditions, deficit irrigation levels, and sowing dates. Overall, the AquaCrop model could be used as a reliable decision-making tool for the field managers and stakeholders.

Incorporating dynamic crop growth processes and management practices into a terrestrial biosphere model for simulating crop production in the United States: Toward a unified modeling framework

Agricultural decision-making by different interest groups (e.g., farmers, development agents and policy makers) usually takes place on different scales (e.g., plot, landscape and country). Currently, tools to assist decision-making are either dedicated to small-scale management guidance or large-scale assessment, which ignore the cross-scale linkages and interactions and thus may not provide robust and consistent guidance and assessment. Here, we developed an advanced agricultural modeling framework by integrating the strengths of conventional crop models in representing crop growth processes and management practices into a terrestrial biosphere model (TBM), the Dynamic Land Ecosystem Model (DLEM), to meet the cross-scale application needs (e.g., adaptation and mitigation). Specifically, dynamic crop growth processes, including crop-specific phenological development, carbon allocation, yield formation, biological nitrogen fixation processes, and management practices such as tillage, cover cropping and genetic improvements, were explicitly represented in DLEM. The new model was evaluated against site-scale observations and the results showed that the model performed generally well, with an average normalized root mean square error of 19.91% for leaf area index and 17.46% for aboveground biomass at the seasonal scale and 14.42% for annual yield. Then the model was applied to simulate corn, soybean, and winter wheat productions in the conterminous United States from 1960 to 2018. The spatial patterns of simulated crop productions were consistent with ground survey data. Our model also captured both the long-term trends and interannual variations of the total national productions of the three crops. This study demonstrates the significance of fusing conventional crop modeling techniques into TBMs to establish a unified modeling framework, which holds the potential to address climate impacts, adaptation and mitigation across varied spatiotemporal scales.

Random Forest for rice yield mapping and prediction using Sentinel-2 data with Google Earth Engine

Accurate information on crop yield prediction is essential for farmers, governments, scientists, and agricultural agencies to make well-informed decisions. Majority of yield prediction methods have been based on data assimilation, which incorporates consecutive observation of canopy development from remote sensing data into model simulations of crop growth processes. But this study used high resolution Sentinel-2 data with combination of different types of secondary data in Random Forest (RF) regression model on different phases of the crop growing season for higher accurate rice yield prediction. For that First, computed crop/non-crop and rice/non-rice crops through RF classifiers were applied on seasonal median composites of Sentinel-2 data for each pixel in the region. Thousands of crop/non-crop labels were collected using an in-house google earth engine (GEE) labeler, and several crop type labels were obtained from various sources during the crop growing seasons. Results demonstrate that sentinel-2 imagery is useful to detect crop/non-crop classes from cropland with more than 85% accuracy, thus it can be used for crop prediction. Furthermore, the Sentinel-2 imagery with secondary data such as environmental, soil and topographic data perform higher accuracy for yield prediction. Its show 0.40 to 1.01 t/ha yield production range at a landscape level. Overall, this study illustrates the Sentinel-2 imagery, GEE platform, advanced classification and rice yield mapping algorithms are enhance the understanding of precision agricultural systems.

A dataset of winter wheat aboveground biomass in China during 2007–2015 based on data assimilation

As a key variable to characterize the process of crop growth, the aboveground biomass (AGB) plays an important role in crop management and production. Process-based models and remote sensing are two important scientific methods for crop AGB estimation. In this study, we combined observations from agricultural meteorological stations and county-level yield statistics to calibrate a process-based crop growth model for winter wheat. After that, we assimilated a reprocessed temporal-spatial filtered MODIS Leaf Area Index product into the model to derive the 1 km daily AGB dataset of the main winter wheat producing areas in China from 2007 to 2015. The validation using ground measurements also suggests the derived AGB dataset agrees well with the filed observations, i.e., the R2 is above 0.9, and the root mean square error (RMSE) reaches 1,377 kg·ha−1. Compared to county-level statistics during 2007–2015, the ranges of R2, RMSE, and mean absolute percentage error (MAPE) are 0.73~0.89, 953~1,503 kg·ha−1, and 8%~12%, respectively. We believe our dataset can be helpful for relevant studies on regional agricultural production management and yield estimation.

Estimating the Impacts of Plant Internal Nitrogen Deficit at Key Top Dressing Stages on Corn Productivity and Intercepted Photosynthetic Active Radiation.

Accurate and timely appraisal of plant nitrogen (N) demand is imperative to regulate the canopy structure and corn production. The strength and time of plant N deficit can be quantified by critical N concentration. The study was aimed to analyze nitrogen nutrition index (NNI), nitrogen deficit content (NDC), plant nitrogen productivity (PNP), and a fraction of intercepted photosynthetic active radiation (FIPAR) across different N treatments and to develop NNI-NDC, NNI-PNP, NNI-FIPAR, NDC-PNP, and NDC-FIPAR relationships from V6 to V12 stages of corn to quantify the suitable PNP and FIPAR values under the optimal plant N condition. Four multi-N rates (0, 75, 90, 150, 180, 225, 270, and 300 kg N ha−1) field experiments were conducted with two cultivars of corn in Henan province of China. Results indicated that N fertilization affected yield, plant biomass, plant N content, and leaf area index. The values of NNI and NDC were from 0.54 to 1.28 kg ha−1 and from −28.13 to 21.99 kg ha−1 under the different treatments of N rate, respectively. The NDC and NNI showed significantly negative relationships from V6 to V12 stages. The values of PNP and FIPAR increased gradually with the crop growth process. The PNP values gradually declined while the FIPAR values of every leaf layer increased with the increase of N supply. The NDC-PNP and NNI-FIPAR relationships were significantly positive; however, the relationships between NNI-PNP and NDC-FIPAR were significantly negative during the vegetative period of corn. The coefficient of determination (R2) based on NNI was better than that on NDC. The FIPAR values were ~0.35, 0.67, and 0.76% at the upper, middle, and bottom of leaf layers, respectively, and PNP values were ~39, 44, and 51 kg kg−1 at V6, V9, and V12 stages, respectively, when NNI and NDC values were equal to 1 and 0 kg ha−1, respectively. This study described the quantitative information about the effect of a plant's internal N deficit on plant N productivity and canopy light intercept. The projected results would assist in predicting the appropriate plant growth status during key N top-dressing stages of corn, which can optimize N application and improve N use efficiency.

Crop-growth-based spatially-distributed optimization model for irrigation water resource management under uncertainties and future climate change

The spatially distributed AquaCrop-multiobjective robust possibilistic programming (distributed AquaCrop-MORPP) model that considers spatially-distributed land, soil and management features has been proposed for irrigation scheduling optimization under uncertain conditions. Compared with traditional simulation-optimization models, it improved the robustness of decision making by optimizing irrigation amounts and dates considering physical crop growth process and the spatial heterogeneity of soil, crop and irrigation. Additionally, it enhanced the AquaCrop-optimization model by handling uncertainties presented as fuzzy and stochastic variables. Moreover, it enhanced the applicability of multi-objective programming (MOP) by balancing contradictory relationships between the objectives of the net economic benefit and field water use efficiency, which were simulated and calculated by the spatially-distributed AquaCrop model. Fifty groups of optimal Pareto solutions were obtained by solving the spatially-distributed AquaCrop-MORPP model using a fast and elitist multi-objective genetic algorithm (NSGA-II) method. Subjective decision-making methods (e.g., Technique for order preference by similarity to an ideal solution (TOPSIS) and prospect theory) were used to select specific alternatives from the Pareto solutions to provide recommendations for managers from different viewpoints. The alternatives that maximized field water use efficiency or net economic benefit separately were selected to cope with emergencies. To obtain optimal irrigation scheduling under future climate change, the RCP4.5 future climate scenario was integrated with the spatially-distributed AquaCrop-MORPP model. The model was verified by applying it to the Yingke Irrigation District (YID), Heihe River Basin (HRB), China. The results showed that the maximum field water use efficiency and net economic benefit in 2021 improved by 24% and 1.3% than those in 2012 for the alternative selected by TOPSIS method. The irrigation scheduling with varied irrigation dates improved by 3.7% and 5% than the irrigation scheduling with fixed irrigation dates for field water use efficiency and net economic benefit, separately. The study provides a framework about how to build spatially-distributed crop simulation-optimization model with consideration of tradeoffs amid multiple objectives to optimize irrigation schedules, and how to select alternatives based on managers’ risk attitudes, and offer a series of Pareto solutions for managers.

Watershed-scale modelling of the irrigated rice farming system at Muda, Malaysia, using the Soil Water Assessment Tool

ABSTRACT Agricultural production in a highly managed watershed is challenging to capture in models due to its complexity and large hydrological, management, and crop data requirements. We evaluated hydro-crop simulation of Malaysia’s largest rice granary using the Soil Water Assessment Tool (SWAT). The 4515 km2 watershed is characterized by dams in the upper sub-watersheds that drain towards 1007 km2 of rice paddies. Monthly averaged streamflow simulation produced Nash-Sutcliffe efficiencies and coefficients of determination between 0.84 and 0.90 in both calibration and validation periods. Meanwhile, the normalized root mean square error and the percent bias for rice yield simulation in off and main seasons of rice planting were within ± 10%. Investigation of rice yield loss processes revealed a stronger control by solar radiation on crop growth processes compared to other hydroclimatic and environmental stresses. Our findings show that SWAT can reliably simulate streamflow and rice growth; however, it may not capture all yield loss processes.

Increased probability and severity of compound dry and hot growing seasons over world's major croplands

Dry and hot extremes are major sources of risk to crop yields, and their impacts are expected to increase under future global warming. The co-occurring dry and hot conditions during crop growing seasons have amplified impacts on crop health that are even larger than the sum of their individual impacts, which may cause crop failure. In this study, we focus on the compound dry and hot growing seasons (hereafter CDHGS) for global wheat, rice, maize and soybean in the period 1951–2020. Total precipitation (TP) and accumulated active temperature (AAT) are used as indicators of overall water stress and heat stress, respectively, at the growing season scale. A copula model is used to construct joint distributions of TP and AAT sequences to investigate the joint behavior of dry and hot conditions during crop growing seasons. Our results indicate that after 1980, the growing seasons of the four crops become drier and more rapidly hotter across the globe, the probability of extreme CDHGS (P(TP ≤ TP25,AAT > AAT75)) increases in more than 80% of global croplands, the severity of CDHGS increases in more than 83% of global croplands, especially in Europe, Central Africa and eastern China. This study provides a global dimension analysis on the changes in compound dry and hot stresses within crops growing seasons in the context of global warming, offering helpful techniques to study the interaction between multi-hazards that occur during crop growth processes, which can effectively contribute to guiding the decision-making processes related to risk reduction and agricultural practices.

Actinomycetes as biostimulants

The article discusses the dynamics of biosynthesis of antibiotic substances of protein nature by local strains isolated from acreage intended for cotton cultivation. Data on the selection of nutrient media of various variations, conditions of cultivation of strains of soil actinomycete Streptomycessp, the dynamics of crop growth and optimization processes in nutrient media for maximum protein formation in the cultivated medium are presented. Highly active representatives of actinomycetes - protein producers have been identified.