Crop Growth Variables Research Articles

Introducing exceptional growth mining-Analyzing the impact of soil characteristics on on-farm crop growth and yield variability.

Sustainable intensification of agriculture requires understanding of the effect of soil characteristics and nutrient supply on crop growth. As farms are increasing in size by acquiring small fields from various farmers, the soil characteristics and nutrient supply might be very different from field to field, while at the same time specific soil properties might limit the nutrient uptake. As a result, there might be a large number of heterogeneous reasons why crop growth varies significantly. New data analysis techniques can help to explain variability in crop growth among fields. This paper introduces Exceptional Growth Mining (EGM) as a first contribution. EGM instantiates the data mining framework Exceptional Model Mining (EMM) such that subgroups of fields can be found that grow exceptionally in terms of three growth parameters (high/low maximum growth, steep/flat linear growth and early/late midpoint of maximum growth). As second contribution, we apply EGM to a case study by analyzing the dataset of a potato farm in the south of the Netherlands. EGM consists of (i) estimating growth curves by applying nonlinear mixed models, (ii) investigating the correlation between the estimated growth parameters, and (iii) applying EMM on these growth curve parameters using a growth curve-specific quality measure. By applying EGM on the data of the potato farm, we obtain the following results: 1) the estimated growth curves represent the variability in potato tuber growth very well (R2 of 0.92), 2) the steepness of the growth curve has a strong correlation with the maximum growth and the midpoint of maximum growth, and the correlation between the midpoint of maximum growth and maximum growth is weak, 3) the subgroup analyses indicate that: high values of K correspond to high maxima; low values of K correspond to low maxima, steep growth curves', and a late midpoint of halfway growth; Mg influences the midpoint of the growth curve; values of B are higher on dry soils with high tuber growth, while low values of B are found on wet soils with high tuber growth; high values of Zn, Mn, and Fe are found in subgroups with low tuber weight, probably related to the soil's low pH. In summary, this paper introduces EGM to obtain understanding in crop response to soil properties and nutrient supply. In addition, EGM provides a way to analyze only small parts of a large dataset, such that the impact of soil factors on growth can be analyzed on a more detailed level than existing methods.

Performance evaluation of the ORYZA_V3 model for rice growth and organ nitrogen content

AbstractThe ORYZA version 3 (ORYZA_V3) model is commonly used for simulating paddy rice (Oryza sativa L.) growth. However, few studies have investigated the performance of the ORYZA_V3 model for predicting in‐season crop growth variables, especially for the nitrogen (N) translocation process among paddy rice organs in the whole growing season, that is, from sowing to harvest stages. This study examines the prediction accuracy of seven paddy rice growth variables based on one cropping season of field experiment. The results show that (1) the ORYZA_V3 model generates accurate prediction of paddy rice leaf area index, above‐ground biomass, and yield, where root mean square errors (RMSEs) are 0.98–1.11 ha/ha, 2360.5–3028.7 kg/ha, and 492.9–515.2 kg/ha, respectively, coefficient of determination (R2) ranges from 0.76 to 1.00, and index of agreement (D‐index) ranges from 0.50 to 0.98. (2) The prediction accuracy of paddy rice leaf and stem N contents is acceptable, where RMSE ranges from 0.0017 to 0.0041 kg/kg, R2 ranges from 0.82 to 0.96, and D‐index ranges from 0.93 to 0.98. (3) The ORYZA_V3 model cannot generate accurate prediction of rice root and panicle N contents, where RMSE ranges from 0.0007 to 0.0022 kg/kg, R2 ranges from 0.01 to 0.20, and D‐index ranges from 0.17 to 0.65. The structural uncertainty in the N translocation module may be a major reason for the unacceptable crop model prediction. This study demonstrated the substantial potential of the ORYZA_V3 model for simulating and understanding paddy rice growth and organ N translocation.

Site‐specific estimation of spatiotemporal crop evapotranspiration through field‐level sensing data

AbstractThis study integrated field‐level sensor data into the FAO‐56 Penman–Monteith algorithm to provide a site‐specific estimate of crop evapotranspiration. This was carried out at two contrasting sites for pea and bean (Manawatū) and barley (Hawke's Bay) crops managed within two irrigation management zones, at each site, under variable‐rate irrigation systems in New Zealand. Daily crop evapotranspiration estimates were calculated using data from a weather station situated at the field site combined with in‐field crop sensing data (spectral reflectance, canopy temperature, and canopy height). In addition, calibrated soil moisture data were used with a soil water balance model to compare estimations of daily crop evapotranspiration with those estimated using the crop sensing method. The results indicated that variable crop responses to different irrigation strategies and soil types provided a good opportunity to quantify different levels of spectral reflectance, canopy temperature, and consequently the estimation of crop water use. The statistical comparisons revealed that the modified FAO‐56 Penman–Monteith using crop sensor data compared well with the more conventional soil water balance approach using soil moisture data (R2 = 0.70, 0.83, 0.91 for barley, pea, and bean, respectively). Overall, the results from this study indicated that crop sensing approaches combined with the FAO‐56 Penman–Monteith model have potential to provide a more easily determined site‐specific field estimation of crop evapotranspiration than other methods, and it can take into consideration the spatiotemporal variability of crop growth in a field.

Impact of crop types and irrigation on soil moisture downscaling in water-stressed cropland regions

Soil moisture (SM) is one of the determining variables of natural ecosystems and human managed regions. The downscaling of coarse spatial-resolution SM data is an important way to obtain high-spatial-resolution data and conduct precise agricultural management. However, the spatial heterogeneity of agricultural management makes it challenging to improve the accuracy of SM data downscaling in croplands. Previously, natural index was considered, such as meteorology, crop growth, topography and soil texture variables, but less so have human agricultural management index. We integrated important agricultural management index (crop type and irrigation) into random forest (RF) model for a downscaling study involving Global Land Data Assimilation System (GLDAS) SM products (from 0.25° to 1 km). The integration of crop type and irrigation improved the downscaling accuracy (R-values of 0.724 and 0.749, respectively) and increased the time series consistency (the Euclidean distance (ED) were reduced by 51% and 20%, respectively) for different soil layers (0–10 cm and 10–40 cm). The best correlations were found for winter wheat and summer maize (both had R-values >0.780). The relative importance of the crop type and irrigation in SM downscaling reached 15% ∼ 18%, behind only to the effects of topography (35%) and meteorology (19% ∼ 22%). This study emphasizes the important impact of human activities on soil moisture in croplands.

Precision Agriculture and Its Future

Natural resources, biotic variables, agro-inputs, and management all impact agricultural production. Uncontrolled use of resources and inputs frequently occurs by farmers, which results in environmental pollution, degradation of land, and financial loss to farmers. The term "precision farming" describes the integration of GIS and GPS tools to provide extensive detailed information on crop growth, crop health, crop yield, water absorption, nutrient levels, topography, and soil variability [1]. Precision agriculture makes use of technology such as sensors, GPS, GIS, Internet of Things, drones, etc., among other things, to optimize the use of natural resources and farm inputs for a given crop production and quality. Agriculture could become more productive and consistent due to digital agriculture and more effective use of resources and time. This article presents the gist of Precision Agriculture along with its components and future implications.

Site-specific nitrogen recommendations’ empirical algorithm for maize crop based on the fusion of soil and vegetation maps

The Oklahoma State University algorithm (OSU) is the most widespread recommendation system to calculate the optimal nitrogen (N) rate in cereal crops. This system is based on the map of a crop vegetation index and N-rich strips in the field, serving as a reference for crop vigour. Based on these inputs, yield potential and in-season crop response to N fertilisation are predicted. The system does not consider other factors influencing the spatial variability of crop growth (e.g., soil), even if the integration of additional data sources could provide more correct N recommendations by considering simultaneously the main drivers of crop growth and production variability. Therefore, the main aims of this study were to modify the OSU algorithm by merging maps of soil electrical conductivity and crop vegetation index (NDRE) to identify management zones instead of using vegetation index alone and to implement and apply the algorithm to define crop response to nitrogen, specific by management zones using a statistical approach instead of N-rich strips in the field. The proposed algorithm was calibrated for maize in northern Italy on four experimental fields in 2021. A comparison among the proposed, the original algorithm and one its previous modification (at Clemson University) was carried out in terms of spatial accuracy and levels of recommended N rates. The proposed algorithm, thanks to the integrated approach, distinguished more in detail different crop responses to N within each management zone and provided N recommendations that match the within-field variability better than the original algorithm and subsequent similar modifications. The new approach resulted in potential average N savings of about 12% compared to uniform management. We conclude that the potential of merging soil and vegetation indices and the definition of N rates with a statistical approach could improve performances and, at the same time, facilitate the adoption of the OSU algorithm. Field validation is needed to confirm the promising results shown in this work.

Prediction of field winter wheat yield using fewer parameters at middle growth stage by linear regression and the BP neural network method

Simple and reliable methods for wheat yield forecasting at the field scale are significant for farmers to formulate effective and timely field management and estimate economic revenue. In this study, the linear regression method (least absolute shrinkage and selection operator regression, LASSO) and BP neural network method (back propagation neural network, BPNN) were chosen for field yield prediction. The four variable types considered are climate, soil moisture, crop growth, and flag leaf photosynthesis. Crop-related data were collected in 12 winter wheat varieties that are widely grown in the North China Plain from 2018 to 2021. The same data set was used for both LASSO and BPNN models’ development and validation. The results show that models based on LASSO method had the determination coefficient (R2) from 0.23 to 0.78, and the root mean square error (RMSE) from 548 to 1023 kg ha−1, while they were 0.66–0.94 and 302–684 kg ha−1 in models based on the BPNN method. This showed that the BPNN method performed better in field wheat yield prediction than the LASSO method. For BPNN method, using only a single variable at the early grain-filling stage had the similar prediction accuracy to using single or comprehensive variables in multiple growth periods, indicating that the field wheat yield can be predicted earlier by BPNN method with fewer indicators. Last, an optimal field wheat prediction model was developed by the BPNN method using crop growth variables (dry matter and leaf area index) at the early grain-filling stage, with the R2 and RMSE of 0.93 and 288 kg ha−1 in the model development stage, respectively, and correspondingly 0.86 and 481 kg ha−1 in the model validation stage. The significant point of this model is that the field wheat yield can be predicted with only two growth indicators approximately 35 days before harvest for 12 mostly cultivated wheat varieties.

Monitoring Key Wheat Growth Variables by Integrating Phenology and UAV Multispectral Imagery Data into Random Forest Model

Rapidly developing remote sensing techniques are shedding new light on large-scale crop growth status monitoring, especially in recent applications of unmanned aerial vehicles (UAVs). Many inversion models have been built to estimate crop growth variables. However, the present methods focused on building models for each single crop stage, and the features generally used in the models are vegetation indices (VI) or joint VI with data derived from UAV-based sensors (e.g., texture, RGB color information, or canopy height). It is obvious these models are either limited to a single stage or have an unstable performance across stages. To address these issues, this study selected four key wheat growth parameters for inversion: above-ground biomass (AGB), plant nitrogen accumulation (PNA) and concentration (PNC), and the nitrogen nutrition index (NNI). Crop data and multispectral data were acquired in five wheat growth stages. Then, the band reflectance and VI were obtained from multispectral data, along with the five stages that were recorded as phenology indicators (PIs) according to the stage of Zadok’s scale. These three types of data formed six combinations (C1–C6): C1 used all of the band reflectances, C2 used all VIs, C3 used bands and VIs, C4 used bands and PIs, C5 used VIs and PIs, and C6 used bands, Vis, and PIs. Some of the combinations were integrated with PIs to verify if PIs can improve the model accuracy. Random forest (RF) was used to build models with combinations of different parameters and evaluate the feature importance. The results showed that all models of different combinations have good performance in the modeling of crop parameters, such as R2 from 0.6 to 0.79 and NRMSE from 10.51 to 15.83%. Then, the model was optimized to understand the importance of PIs. The results showed that the combinations that integrated PIs showed better estimations and the potential of using PIs to minimize features while still achieving good predictions. Finally, the varied model results were evaluated to analyze their performances in different stages or fertilizer treatments. The results showed the models have good performances at different stages or treatments (R2 > 0.6). This paper provides a reference for monitoring and estimating wheat growth parameters based on UAV multispectral imagery and phenology information.

Biomass estimation of spring wheat with machine learning methods using UAV-based multispectral imaging

ABSTRACT Remote biomass estimation can benefit agricultural practices in several ways, especially larger areas since it does not require local measurements. The advances of the last few decades in machine learning techniques have created new possibilities for estimating aboveground biomass. A pipeline was established from image acquisition to modelling shoot biomass of two wheat cultivars used in Southern Brazil (TBIO Toruk and BRS Parrudo). A UAV was used to acquire multispectral images with high spatial resolution to calculate vegetation indices (VIs). These VIs along with machine learning approaches are used to model the measured biomass of crops in different growth phases. To correlate the wheat images with measured shoot dry biomass, the following regression models were investigated: random forest, support vector regression, and artificial neural networks. An experiment was designed and conducted at the Agriculture Experimental Station of the Federal University of Rio Grande do Sul (EEA/UFRGS) to assess wheat growth. Variability in crop growth was created for all test areas by varying nitrogen availability. To determine shoot biomass, plants were sampled at three different crop growth stages: V6 (stage of six fully developed leaves), three nodes, and flowering. Our results indicate the importance of the radiometric calibration used. Also, the features extracted from images, such as the VIs combined with machine learning models can be used in precision agriculture for predicting the spatial variability of shoot biomass. The best model for Brazilian wheat cultivars was an artificial neural network with of 0.90, RMSE of 0.83t/ha, and nRMSE of 8.95%. We also found a strong correlation between ground NDVI with image-based NDVI, with an of 0.84.

Wood ash as a vegetative-growth promoter in soils with subsurface compaction

ABSTRACT Subsurface soil compaction and nutritional stress are among the main factors that limit the yield of crops. Using forest residues, such as wood ash, is a viable option in the chemical recovery of soils and can promote vigorous root development in soils with subsurface compaction. The objective of this study was to indicate the most adequate dose of wood ash for efficient management of this residue applied in rotational crops cultivated in soils with subsurface compaction. Safflower plants (Carthamus tinctorius), a rotational crop with a deep taproot system, were grown in clay soil fertilized with different doses of ash and with induced levels of compaction in the subsurface layer. The experiment was conducted in a randomized block design, under a 4 × 5 factorial scheme, composed of four doses of wood ash (8.0, 16.0, 24.0, and 32.0 g dm-3) and five levels of soil bulk density (1.0, 1.2, 1.4, 1.6, and 1.8 kg dm-3), with four replicates. Crop growth variables (plant height, number of leaves, stem diameter, and SPAD chlorophyll index) were evaluated at 15, 45, and 75 days after emergence. The results indicated that soil compaction was the most limiting factor to the vegetative development of safflower, regardless of the ash dose. The interaction between the wood ash dose and bulk density, when present, showed that the best growth response occurred for ash dose of 25 g dm-3 for a soil bulk density of 1.2 kg dm-3.

Above-ground Biomass Wheat Estimation: Deep Learning with UAV-based RGB Images

ABSTRACT Evaluation of biomass is essential in agriculture to delineate crop management practices, and this is usually done manually, which is time-consuming and destructive. This work proposes an artificial neural network and convolutional neural network to estimate the above-ground biomass (AGB) of wheat using visible spectrum images captured by an unmanned aerial vehicle. The utilized dataset has two Brazilian wheat types, called Parrudo and Toruk. Furthermore, the experimental area has variability in crop growth by varying the amount of nitrogen. To determine AGB, samples of plants were collected at three different crop growth stages, approximately a month apart, making our database spatial and temporal variability. We have shown the feasibility of developing a regression model using RGB images for biomass estimation for two wheat types. The best results for ANN were 489.5, 826.4, and 0.9056 for MAE, RMSE, and , respectively. In CNN, MAE = 699.2, RMSE = 940.5, and = 0.9065. These results show high accuracy in estimation of biomass, and the shows good estimation and generalization capacity. The results demonstrate that our methodology can be used in precision agriculture to predict the spatial and temporal variability of AGB.

Simulation of maize crop growth using an improved crop model considering the disintegrated area of biodegradable film

• An improved crop growth model (FCM) under biodegradable film mulching was developed. • FCM model performed well in capturing the temporal trends in crop growth dynamics. • Biodegradable film mulching results in a slight decrease of crop yield. • An irrigation depth of 22.5 mm as optimal treatment was recommended in arid region. • Disintegrated rate should not exceed 63 % and 38 % in 2018 and 2019, respectively. Biodegradable film is recognized as a promising alternative to plastic film within crop mulching. This is because biodegradable film disintegrates into water and carbon dioxide through soil microorganisms-facilitated catalytic decomposition during the crop growth season. However, biodegradable film mulching (BFM) also has some disadvantages, such as the water and heat conservation being poor during nighttime period. Crop growth and yield is reduced by a suboptimal distribution of soil water and temperature. Therefore, there is value in assessing the effects of BFM on crop growth during daytime and nighttime periods, and particularly under different irrigation levels. This study developed a crop growth model (FCM) considering the disintegrated area of biodegradable film mulching to capture crop growth dynamics under different soil water contents and mulching areas. The disintegrated area of biodegradable film was determined by an image method. The model was firstly calibrated and validated using observed data collected over two years (2018 and 2019) under three mulching treatments [plastic film mulching (PFM), biodegradable film mulching (BFM), and no film mulching (NFM)] and three irrigation levels (irrigation depths of 30, 22.5, and 15 mm). The model was then used to evaluate crop growth rate ( k ), crop yield ( CY ), and water use efficiency (WUE) during daytime and nighttime periods. The results showed that the simulation accuracy of the FCM model was sufficiently high under different treatments, with a root mean square error ( RMSE) of 284.6 kg ha –1 . The benefits of BFM for crop growth were similar to that of PFM under a low range of effective accumulated soil temperature (EAST). However, differences in crop growth variables between PFM and BFM increased with an increase in EAST. Average leaf area index ( LAI ) and aboveground dry biomass ( AB ) during both years increased by 16.9 % and 39.2 % under PFM compared to that under BFM, respectively. In general, the harvest index ( HI ), CY , and water use efficiency (WUE) under BFM were lower than that under PFM. There were comparable variations in crop growth under different irrigation levels. The highest WUE occurred in the BFM 22.5 treatment at 44.5 kg ha –1 mm –1 . Therefore, this study recommends an irrigation depth of 22.5 mm as an optimal irrigation treatment in an arid region.

The use of terrestrial LiDAR to monitor crop growth and account for within-field variability of crop coefficients and water use

Monitoring the spatio-temporal distribution of crop height and biomass is important for crop management in terms of applying irrigation, fertilizers and pesticides. This paper reports the performance of a terrestrial laser scanner (TLS) for measuring crop height and biomass of a bean crop (Phaseolus vulgaris., cv. ‘Contender’); and crop height for barley (Hordeum vulgare., cv. ‘Carfields CKS1′), and pea (Pisum sativum., cv. ‘Massey’) crops, grown under field conditions. In addition, a method is presented that improves spatio-temporal estimates of crop coefficient (Kc) and crop evapotranspiration (ETc) using TLS data combined with NDVI data (“TLVI method”), compared with standard FAO56 model estimates. Evidence is provided to show that the TLVI method gives accurate estimates of crop growth patterns due to spatial variations in soil, microclimate and plant health, providing a valuable tool for precision crop management.The TLS-derived canopy height method showed a significantly high correlation and low biases and errors with the manually measured canopy height (R2 = o.95, RMSE = 5.85 for barley; R2 = 0.93, RMSE = 3.01 for pea; R2 = 0.91, RMSE = 1.82 for bean). The TLS method also showed potential to estimate bean biomass (R2 = 0.70, RMSE = 37.56). In-field sensor measurements of canopy height and NDVI were used to estimate site specific crop factors required for accurate estimation of daily evapotranspiration losses. These estimates showed general agreement with those predicted by standard dual FAO56 crop coefficients, but in addition were able to detect the spatial variability in crop growth and water requirements which were largely controlled by soil differences.Overall, the results showed that the TLS method has the potential to measure crop height surface and that the TLVI method provides estimates of ETc with a high spatio-temporal resolution for different crops. This approach can be considered a very promising tool for site-specific management.

Effect of different doses of acid milk serum on growth in rice culture and on soil chemical attributes

The culture of rice has great potential to constitute the production systems of annual perennial crops, mainly in small properties. This allows this system to integrate with other activities such as the dairy industry, taking advantage of the milk by-product, reducing the environmental liability for disposal in the environment. Therefore, the objective was to evaluate the growth and productivity of rice grains under different doses of acid milk serum (0 m3 ha-1, 5 m3 ha-1, 10 m3 ha-1, 20 m3 ha-1, 50 m3 ha-1, 100 m3 ha-1, and 150 m3 ha-1), in soil applications with monitoring of fertility and electrical conductivity levels. The rice was sown in the UFLA experimental field, using the BRSMG Esmeralda lineage. Seven different doses were used, divided into two applications via soil. No cover fertilization was carried out. Yield and crop growth variables were determined by monitoring the nutrient content in the soil profile and the sodification and salinization capacity of the residue. It was found that the increase in serum doses increased the electrical conductivity and the percentage of exchangeable sodium by 1.75 and 7.75 compared to the control, but did not cause the process of sodification and salinization of the soil. As acid milk serum is a significant source of potassium, its use in the soil did not promote significant grain yield. It was concluded that the acid milk serum in the doses used did not cause sodification or salinization of the soil. This residue is the safe dose that can be used as potassium and nitrogen. The acid milk serum in the tested conditions did not increase the growth of the culture.

Agricultural Monitoring Using Polarimetric Decomposition Parameters of Sentinel-1 Data

The time series of synthetic aperture radar (SAR) data are commonly and successfully used to monitor the biophysical parameters of agricultural fields. Because, until now, mainly backscatter coefficients have been analysed, this study examines the potentials of entropy, anisotropy, and alpha angle derived from a dual-polarimetric decomposition of Sentinel-1 data to monitor crop development. The temporal profiles of these parameters are analysed for wheat and barley in the vegetation periods 2017 and 2018 for 13 fields in two test sites in Northeast Germany. The relation between polarimetric parameters and biophysical parameters observed in the field is investigated using linear and exponential regression models that are evaluated using the coefficient of determination (R2) and the root mean square error (RMSE). The performance of single regression models is furthermore compared to those of multiple regression models, including backscatter coefficients in VV and VH polarisation as well as polarimetric decomposition parameters entropy and alpha. Characteristic temporal profiles of entropy, anisotropy, and alpha reflecting the main phenological changes in plants as well as the meteorological differences between the two years are observed for both crop types. The regression models perform best for data from the phenological growth stages tillering to booting. The highest R2 values of the single regression models are reached for the plant height of wheat related to entropy and anisotropy with R2 values of 0.64 and 0.61, respectively. The multiple regression models of VH, VV, entropy, and alpha outperform single regression models in most cases. R2 values of multiple regression models of plant height (0.76), wet biomass (0.7), dry biomass (0.7), and vegetation water content (0.69) improve those of single regression models slightly by up to 0.05. Additionally, the RMSE values of the multiple regression models are around 10% lower compared to those of single regression models. The results indicate the capability of dual-polarimetric decomposition parameters in serving as meaningful input parameters for multiple regression models to improve the prediction of biophysical parameters. Additionally, their temporal profiles indicate phenological development dependent on meteorological conditions. Knowledge about biophysical parameter development and phenology is important for farmers to monitor crop growth variability during the vegetation period to adapt and to optimize field management.

Topography Controls N2O Emissions Differently during Early and Late Corn Growing Season

Topography affects soil hydrological, pedological, and biochemical processes and may influence nitrous oxide (N2O) emissions into the atmosphere. While N2O emissions from agricultural fields are mainly measured at plot scale and on flat topography, intrafield topographical and crop growth variability alter soil processes and might impact N2O emissions. The objective of this study was to examine the impact of topographical variations on crop growth period dependent soil N2O emissions at the field scale. A field experiment was conducted at two agricultural farms (Baggs farm; BF and Research North; RN) with undulating topography. Dominant slope positions (upper, middle, lower and toeslope) were identified based on elevation difference. Soil and gas samples were collected from four replicated locations within each slope position over the whole corn growing season (May–October 2019) to measure soil physio-chemical properties and N2O emissions. The N2O emissions at BF ranged from −0.27 ± 0.42 to 255 ± 105 g ha−1 d−1. Higher cumulative emissions were observed from the upper slope (1040 ± 487 g ha−1) during early growing season and from the toeslope (371 ± 157 g ha−1) during the late growing season with limited variations during the mid growing season. Similarly, at RN farm, (emissions ranged from −0.50 ± 0.83 to 70 ± 15 g ha−1 d−1), the upper slope had higher cumulative emissions during early (576 ± 132 g ha−1) and mid (271 ± 51 g ha−1) growing season, whereas no impact of slope positions was observed during late growing season. Topography controlled soil and environmental properties differently at different crop growth periods; thus, intrafield variability must be considered in estimating N2O emissions and emission factor calculation from agricultural fields. However, due to large spatial variations in N2O emissions, further explorations into site-specific analysis of individual soil properties and their impact on N2O emissions using multiyear data might help to understand and identify hotspots of N2O emissions.

Trends in Satellite Remote Sensing for Precision Agriculture

Remote sensing is an essential tool for precision agriculture, allowing producers and crop consultants to monitor spatial and temporal variability in soil properties, crop growth, and crop stress from nutrients, weeds, disease, insects, or water. During the last decade, the number and capabilities of multispectral satellite remote‐sensing products have increased dramatically. This article provides an overview of some of the trends in satellite remote sensing for precision agriculture. Earn 0.5 CEUs in Crop Management by reading this article and taking the quiz at www.certifiedcropadviser.org/education/classroom/classes/756.

The 13C Discrimination of Crops Identifies Soil Spatial Variability Related to Water Shortage Vulnerability

Spatial variability of crop growth and yields is the result of many interacting factors. The contribution of the factors to variable yields is often difficult to separate. This work studied the relationships between the 13C discrimination (Δ13C) of plants and the spatial variability of field soil conditions related to impacts of water shortage on crop yield. The 13C discrimination, the indicator of water shortage in plants, 15N (δ15N) discrimination, and nitrogen (N) content were determined in grains of winter wheat, spring barley, and pea. The traits were observed at several dozens of grid spots in seven fields situated in two regions with different soil and climate conditions between the years 2017 and 2019. The principles of precision agriculture were implemented in some of the studied fields and years by variable rate nitrogen fertilization. The Δ13C significantly correlated with grain yields (correlation coefficient from 0.66 to 0.94), with the exception of data from the wetter year 2019 at the site with higher soil water capacity. The effect of drought was demonstrated by statistically significant relationships between Δ13C in dry years and soil water capacity (r from 0.46 to 0.97). The significant correlations between Δ13C and N content of seeds and soil water capacity agreed with the expected impact of water shortage on plants. The 13C discrimination of crop seeds was confirmed as a reliable indicator of soil spatial variability related to water shortage. Stronger relationships were found in variably fertilized areas.

Optimizing water and nitrogen application for neglected horticultural species in tropical sub-humid climate areas: A case of African eggplant (Solanum aethiopicum L.)

African eggplant, a traditional and important nutrient-dense crop to Tanzania’s nutrition and food security. However, yields remain low as a result of sub-optimal irrigation and fertilizer practices. To reduce the yield gap, a randomized split-plot design set up with irrigation as a main and nitrogen (N) treatments as a sub-factor. The irrigation regimes were 100 % (I100), 80 % (I80) and 60 % (I60) of crop water requirements whilst nitrogen levels were 250 kg N/ha (F100), 187 kg N/ha (F75), 125 kg N/ha (F50) and 0 kgN/ha (F0). The study evaluated the effect of irrigation water and N on crop growth variables and yield, fruit quality, WUE and NUE. The study showed the importance of combining different irrigation performance indicators which responds to different levels of water and nitrogen to evaluate and assess suitable irrigation and fertilizer strategies for African eggplant. The crop growth variables (plant height and LAI) had a good correlation with fruit yield (R2 = 0.6 and 0.8). The fruit quality was best performed by 100 % water in combination with 75 % N treatment. The best WUE and NUE was attained at 80 % and 100 % levels of water in combination with 75 % N. However, minimizing trade-offs between the various indicators, the optimal application for African eggplant would likely be around 80 % of the total irrigation requirement and 75 % of the N requirement in sandy clay loam soils under tropical sub-humid conditions.

A Strong Linkage between Seasonal Crop Growth and Groundwater Storage Variability in India

AbstractGroundwater is rapidly depleting in India primarily because of pumping for irrigation. However, the crucial role of crop growth at annual and seasonal time scales in groundwater storage variability remains mostly unexplored. Using the data from the Gravity Recovery Climate Experiment (GRACE) satellites and well observations, we show that crop growth is negatively correlated with groundwater storage at annual and seasonal time scales in north India. Precipitation is positively associated with groundwater storage variability at the yearly time scale in north-central India (NCI) and south India (SI). In contrast, precipitation is negatively correlated with groundwater storage from the GRACE satellites in northwest India (NWI). The negative correlation between precipitation and groundwater from the GRACE in NWI is primarily due to groundwater depletion due to anthropogenic pumping from deep aquifers. Precipitation and groundwater storage from the well observations are positively correlated in all the three regions, indicating the influence of precipitation on shallow aquifers. Analysis of the two main crop growing seasons (Rabi and Kharif) showed that crop growth is negatively related to groundwater storage in both Kharif (June–September) and Rabi seasons in north India (NWI and NCI). Groundwater contributes more than precipitation in NCI during the Kharif season and in NWI and SI during the Rabi season. Granger’s causality test showed that groundwater is a significant contributor to crop growth in NWI and NCI in both Kharif and Rabi seasons. Our results highlight the need for agricultural water management in both the crop growing seasons in north India for reducing the rapid groundwater depletion.