Information For Crop Management Research Articles

Prediction of spatial heterogeneity in nutrient-limited sub-tropical maize yield: Implications for precision management in the eastern Indo-Gangetic Plains

Knowledge of the factors influencing nutrient-limited subtropical maize yield and subsequent prediction is crucial for effective nutrient management, maximizing profitability, ensuring food security, and promoting environmental sustainability. We analyzed data from nutrient omission plot trials (NOPTs) conducted in 324 farmers' fields across ten agroecological zones (AEZs) in the Eastern Indo-Gangetic Plains (EIGP) of Bangladesh to explain maize yield variability and identify variables controlling nutrient-limited yields. An additive main effect and multiplicative interaction (AMMI) model was used to explain maize yield variability with nutrient addition. Interpretable machine learning (ML) algorithms in automatic machine learning (AutoML) frameworks were subsequently used to predict attainable yield relative nutrient-limited yield (RY) and to rank variables that control RY. The stack-ensemble model was identified as the best-performing model for predicting RYs of N, P, and Zn. In contrast, deep learning outperformed all base learners for predicting RYK. The best model's square errors (RMSEs) were 0.122, 0.105, 0.123, and 0.104 for RYN, RYP, RYK, and RYZn, respectively. The permutation-based feature importance technique identified soil pH as the most critical variable controlling RYN and RYP. The RYK showed lower in the eastern longitudinal direction. Soil N and Zn were associated with RYZn. The predicted median RY of N, P, K, and Zn, representing average soil fertility, was 0.51, 0.84, 0.87, and 0.97, accounting for 44, 54, 54, and 48% upland dry season crop area of Bangladesh, respectively. Efforts are needed to update databases cataloging variability in land type inundation classes, soil characteristics, and INS and combine them with farmers' crop management information to develop more precise nutrient guidelines for maize in the EIGP.

Predicting wheat yield gap and its determinants combining remote sensing, machine learning, and survey approaches in rainfed Mediterranean regions of Morocco

Wheat plays a crucial role in Morocco’s food security, economic stability, and livelihoods of farming communities. Assessing key vegetation indices (as yield predictors), along with understanding potential yield, yield gap, and major determinants for this gap at regional and national scales, is vital for improving food security with resilience in variable climatic conditions. Analysing the yield gap and its causes during drought and optimal weather conditions can reduce crop failure risks and enhance productivity specially in variable rainfed production systems. This study aimed to develop scalable methodology to predict field- and landscape-level yield and yield gaps for wheat and their underlying causes examplifying Morocco’s rainfed production environment combining remote sensing, machine learning, and ground information. By analysing six vegetation indices (EVI2, CGVI, MSR, NDVI, OSAVI, and RVI) derived from Sentinel-2 satellite imagery (10 m resolution) over three successive growing seasons (2018–2019, 2019–2020, and 2020–2021), the study employed advanced vegetation index models for accurate prediction of wheat yields and yield gaps at plot and on a larger regional scale within the Rabat-Sale-Kenitra region. To identify the determinants of yield gap, climate and soil datasets were merged with crop management information and the random forest model was fine-tuned and assessed for each season and cumulatively. The findings highlighted that RVI, GCVI, and NDVI vegetation indices were particularly effective in predicting wheat yields, showing the highest R2 and the lowest prediction errors (RMSE). Such predictive methodologies are crucial for policymakers to proactively plan and mitigate risk minimization and adaption plans at regional and national levels. The models predicted rainfed potential yields of 5.99, 1.53, and 4.66 t ha−1, with corresponding yield gap of 3.38, 0.73, and 1.58 t ha−1 for the seasons of 2018/2019 (favorable); 2019/2020 (drought) and 2020/2021 (favorable), respectively. Across three periods, critical factors determining yield include soil moisture, total rainfall during the crop growing period, evapotranspiration, and soil texture and carbon content. To minimize drought risks and maximize benefits during variable rainfall conditions, it is essential to implement pre-season drought forecasts, customize seeding dates based on soil moisture, adopt technologies that enhance soil moisture retention, and utilize climate-adapted farming practices in the semi-arid and arid rainfed regions.

Impact of irrigation management on crop water footprint reduction using RZWQM2 in Baghdad, Iraq

Water scarcity becomes a serious global challenge in several world regions and particularly in the Middle East. Appropriate irrigation practice is critical for improving crop yield and alleviating crop water footprint (WF). To alleviate water scarcity, the possibility of reducing wheat and barley water footprint has been examined through alternative surface irrigation practices. The WF was compared under farmers’ irrigation practice and experimental irrigation practice to discover the impact of irrigation practices on the reduction of WF in the west of Baghdad. Weather data and crop management information were collected from 2016 to 2020 for the farmer’s fields in the study area as well as for the experimental field. The Root Zone Water Quality Model (RZWQM2) was used for estimating wheat and barley evapotranspiration. The study results showed that the crop WF was well estimated using the RZWQM2 due to the model capability and accuracy for estimating the impact of field management on crop evapotranspiration and crop water use. Experimental irrigation practice could improve crop yield, water use efficiency, and water profitability by up to 28%, 35%, and 35%, respectively; while the WF was reduced by 35%, compared to the farmers’ irrigation practice. The WF of the wheat crop was lower than the barley WF due to the low barley production.

A Non-Destructive Time Series Model for the Estimation of Cherry Coffee Production

Coffee plays a key role in the generation of rural employment in Colombia. More than 785,000 workers are directly employed in this activity, which represents the 26% of all jobs in the agricultural sector. Colombian coffee growers estimate the production of cherry coffee with the main aim of planning the required activities, and resources (number of workers, required infrastructures), anticipating negotiations, estimating, price, and foreseeing losses of coffee production in a specific territory. These important processes can be affected by several factors that are not easy to predict (e.g., weather variability, diseases, or plagues.). In this paper, we propose a non-destructive time series model, based on weather and crop management information, that estimate coffee production allowing coffee growers to improve their management of agricultural activities such as flowering calendars, harvesting seasons, definition of irrigation methods, nutrition calendars, and programming the times of concentration of production to define the amount of personnel needed for harvesting. The combination of time series and machine learning algorithms based on regression trees (XGBOOST, TR and RF) provides very positive results for the test dataset collected in real conditions for more than a year. The best results were obtained by the XGBOOST model (MAE = 0.03; RMSE = 0.01), and a difference of approximately 0.57% absolute to the main harvest of 2018.

Nonlinear models in the height description of the Rhino sunflower cultivar

ABSTRACT: Sunflower produces achenes and oil of good quality, besides serving for production of silage, forage and biodiesel. Growth modeling allows knowing the growth pattern of the crop and optimizing the management. The research characterized the growth of the Rhino sunflower cultivar using the Logistic and Gompertz models and to make considerations regarding management based on critical points. The data used come from three uniformity trials with the Rhino confectionery sunflower cultivar carried out in the experimental area of the Federal University of Santa Maria - Campus Frederico Westphalen in the 2019/2020 agricultural harvest. In the first, second and third trials 14, 12 and 10 weekly height evaluations were performed on 10 plants, respectively. The data were adjusted for the thermal time accumulated. The parameters were estimated by ordinary least square’s method using the Gauss-Newton algorithm. The fitting quality of the models to the data was measured by the adjusted coefficient of determination, Akaike information criterion, Bayesian information criterion, and through intrinsic and parametric nonlinearity. The inflection points (IP), maximum acceleration (MAP), maximum deceleration (MDP) and asymptotic deceleration (ADP) were determined. Statistical analyses were performed with Microsoft Office Excel® and R software. The models satisfactorily described the height growth curve of sunflower, providing parameters with practical interpretations. The Logistics model has the best fitting quality, being the most suitable for characterizing the growth curve. The estimated critical points provide important information for crop management. Weeds must be controlled until the MAP. Covered fertilizer applications must be carried out between the MAP and IP range. ADP is an indicator of maturity, after reaching this point, the plants can be harvested for the production of silage without loss of volume and quality.

Remote Sensing-Based Quantification of the Summer Maize Yield Gap Induced by Suboptimum Sowing Dates over North China Plain

Estimating yield potential (Yp) and quantifying the contribution of suboptimum field managements to the yield gap (Yg) of crops are important for improving crop yield effectively. However, achieving this goal on a regional scale remains difficult because of challenges in collecting field management information. In this study, we retrieved crop management information (i.e., emerging stage information and a surrogate of sowing date (SDT)) from a remote sensing (RS) vegetation index time series. Then, we developed a new approach to quantify maize Yp, total Yg, and the suboptimum SDT-induced Yg (Yg0) using a process-based RS-driven crop yield model for maize (PRYM–Maize), which was developed in our previous study. PRYM–Maize and the newly developed method were used over the North China Plain (NCP) to estimate Ya, Yp, Yg, and Yg0 of summer maize. Results showed that PRYM–Maize outputs reasonable estimates for maize yield over the NCP, with correlations and root mean standard deviation of 0.49 ± 0.24 and 0.88 ± 0.14 t hm−2, respectively, for modeled annual maize yields versus the reference value for each year over the period 2010 to 2015 on a city level. Yp estimated using our new method can reasonably capture the spatial variations in site-level estimates from crop growth models in previous literature. The mean annual regional Yp of 2010–2015 was estimated to be 11.99 t hm−2, and a Yg value of 5.4 t hm−2 was found between Yp and Ya on a regional scale. An estimated 29–42% of regional Yg in each year (2010–2015) was induced by suboptimum SDT. Results also show that not all Yg0 was persistent over time. Future studies using high spatial-resolution RS images to disaggregate Yg0 into persistent and non-persistent components on a small scale are required to increase maize yield over the NCP.

Estimating the Gross Primary Production and Evapotranspiration of Rice Paddy Fields in the Sub-Tropical Region of China Using a Remotely-Sensed Based Water-Carbon Coupled Model

Rice serves as the staple food for over 50% of the global population. Remotely-sensed based estimation of the gross primary production (GPP) and evapotranspiration (ET) of rice paddy fields is essential to assess global food security. In this study, we tested the application of a recently proposed remotely-sensed based water-carbon coupled model (PML-V2) in the lower reaches of the Poyang Lake plain, which is one of the nine production bases for crops in China. Evaluation using the eddy covariance measurements showed that, after parameter localization, the model reproduced the seasonal variations of GPP and ET for both the early rice and the late rice. The model performed reasonably well in the validation period because the key parameters (e.g., the quantum efficiency and the stomatal conductance coefficient) exhibited predictable seasonal variations. At the regional scale, the spatial distribution in multi-year GPP of rice (1365 ± 326 gCm−2year−1) can be explained by the vegetation cover fraction (R2 > 0.9); in comparison, the multi-year ET (1003 ± 65 mm/year) exhibits smaller spatial variations due to the high evaporation rate of the saturated soil surface of paddy fields. The water use efficiency of rice in this region varies around 1.35 gC/kgH2O with a standard deviation of 0.30. Our study shows that GPP and ET of rice can be estimated by remote sensing models without detailed crop management information, which is usually unavailable at regional scales.

Development of an Online Tool for Tracking Soil Nitrogen to Improve the Environmental Performance of Maize Production

Freshwater nitrogen (N) pollution is a significant sustainability concern in agriculture. In the U.S. Midwest, large precipitation events during winter and spring are a major driver of N losses. Uncertainty about the fate of applied N early in the growing season can prompt farmers to make additional N applications, increasing the risk of environmental N losses. New tools are needed to provide real-time estimates of soil inorganic N status for corn (Zea mays L.) production, especially considering projected increases in precipitation and N losses due to climate change. In this study, we describe the initial stages of developing an online tool for tracking soil N, which included, (i) implementing a network of field trials to monitor changes in soil N concentration during the winter and early growing season, (ii) calibrating and validating a process-based model for soil and crop N cycling, and (iii) developing a user-friendly and publicly available online decision support tool that could potentially assist N fertilizer management. The online tool can estimate real-time soil N availability by simulating corn growth, crop N uptake, soil organic matter mineralization, and N losses from assimilated soil data (from USDA gSSURGO soil database), hourly weather data (from National Weather Service Real-Time Mesoscale Analysis), and user-entered crop management information that is readily available for farmers. The assimilated data have a resolution of 2.5 km. Given limitations in prediction accuracy, however, we acknowledge that further work is needed to improve model performance, which is also critical for enabling adoption by potential users, such as agricultural producers, fertilizer industry, and researchers. We discuss the strengths and limitations of attempting to provide rapid and cost-effective estimates of soil N availability to support in-season N management decisions, specifically related to the need for supplemental N application. If barriers to adoption are overcome to facilitate broader use by farmers, such tools could balance the need for ensuring sufficient soil N supply while decreasing the risk of N losses, and helping increase N use efficiency, reduce pollution, and increase profits.

Assimilation of Remote Sensing Data into Crop Growth Model for Yield Estimation: A Case Study from India

Crop yield estimation is important to inform logistics management such as the prescription of nutrient inputs, financing, storage and transport, marketing as well as to inform for crop insurance appraisals due to loss incurred by abiotic and biotic stresses. In this study, we used a suite of methods to assess yields at the village level (< 5 km2) using remote sensing technology and crop modeling in Indian states of Telangana, Andhra Pradesh and Odisha. Remote sensing products were generated using Sentinel-2 and Landsat 8 time series data and calibrated with data collected from farmers’ fields. We derived maps showing spatial variation in crop extent, crop growth stages and leaf area index (LAI), which are crucial in yield assessment. Crop classification was performed on Sentinel-2 time series data using spectral matching techniques (SMTs) and crop management information collected from field surveys along with ground data. The locations of crop cutting experiments (CCEs) was identified based on crop extent maps. LAI was derived based on the SAVI (soil-adjusted vegetation index) equation were using Landsat 8-time series data. We used the technique of re-parametrization of crop simulation models based on the several iterations using remote sensing leaf area index (LAI). The data assimilation approach helps in fine-tuning the initial parameters of the crop growth model and improving simulation with the help of remotely sensed observations. Results clearly show a good correlation between observed and simulated crop yields (R2 is greater than 0.7) for all the crops studied. Our study showed that by assimilation of remotely sensed data in to crop models, crop yields at harvest could be successfully predicted.

Crop Classification Based on Temporal Signatures of Sentinel-1 Observations over Navarre Province, Spain

Crop classification provides relevant information for crop management, food security assurance and agricultural policy design. The availability of Sentinel-1 image time series, with a very short revisit time and high spatial resolution, has great potential for crop classification in regions with pervasive cloud cover. Dense image time series enable the implementation of supervised crop classification schemes based on the comparison of the time series of the element to classify with the temporal signatures of the considered crops. The main objective of this study is to investigate the performance of a supervised crop classification approach based on crop temporal signatures obtained from Sentinel-1 time series in a challenging case study with a large number of crops and a high heterogeneity in terms of agro-climatic conditions and field sizes. The case study considered a large dataset on the Spanish province of Navarre in the framework of the verification of Common Agricultural Policy (CAP) subsidies. Navarre presents a large agro-climatic diversity with persistent cloud cover areas, and therefore, the technique was implemented both at the provincial and regional scale. In total, 14 crop classes were considered, including different winter crops, summer crops, permanent crops and fallow. Classification results varied depending on the set of input features considered, obtaining Overall Accuracies higher than 70% when the three (VH, VV and VH/VV) channels were used as the input. Crops exhibiting singularities in their temporal signatures were more easily identified, with barley, rice, corn and wheat achieving F1-scores above 75%. The size of fields severely affected classification performance, with ~14% better classification performance for larger fields (&gt;1 ha) in comparison to smaller fields (&lt;0.5 ha). Results improved when agro-climatic diversity was taken into account through regional stratification. It was observed that regions with a higher diversity of crop types, management techniques and a larger proportion of fallow fields obtained lower accuracies. The approach is simple and can be easily implemented operationally to aid CAP inspection procedures or for other purposes.

SPATIALISATION OF RICE GROWTH AND YIELD MODEL USING OPTICAL AND SAR DERIVED BIOPHYSICAL PARAMETERS

Abstract. Integration of remote sensing derived biophysical parameters with process based crop growth simulation model is an emerging technology with diversified application for crop insurance as well as precision farming. Basically the crop growth simulation models are point based which simulate crop growth and yield as a function of soil, weather and crop management factors at a daily time scale. The temporal dimension of the crop growth model is supplemented by the spatial information on crop coverage and condition generated from remote sensing satellite derived biophysical parameters. In the present study an attempt is made to simulate pixel wise rice crop growth, condition and yield using Sentinel1 SAR, AWiFS/Landsat8 OLI and MODIS data with CERES rice growth simulation model on DSSAT platform. Temporal SAR data provides pixel wise start of season for rice crop at a spatial resolution of 10 m. Spectral indices derived from Landsat8/AWiFS and MODIS optical images are used for characterisation of rice growth environment and at the same time these indices are used to generate crop management information like irrigation and sowing dates. Bhadrak district of Odisha in the eastern coast of India is selected as the study area based on the prevalence of diversified rice growing environments. Sufficient in season field data are collected for checking the accuracy of rice map as well as for calibration and validation of the crop growth model. In season rice growth is initialised using SAR derived staggered sowing dates along with daily weather data and soil inputs. As CERES crop growth models are running well on DSSAT environment, rice crop growth simulation is carried out using multi date SAR images on DSSAT platform. The output of this study is maps depicting the spatial variability in rice area, staggered sowing dates and irrigation in the study region. These in season information are crucial for decision making particularly for crop insurance related activities.

Climate change, agricultural inputs, cropping diversity, and environment affect soil carbon and respiration: A case study in Saskatchewan, Canada

Climate change, agricultural inputs, cropping diversity, and environment have seldom been combined in analyses of soil organic carbon (SOC), and soil C respired through microbial respiration (MR). This modeling study assessed SOC and MR simulated with the Environmental Policy Integrated Climate (EPIC) model for historical weather (1971–2000) and future climate scenarios (2041–2070) for the Alternative Cropping Systems (ACS) study research site in Saskatchewan, Canada. Nineteen years of field and crop management information from the 1994–2013 ACS study were used to validate and provide parameters to the EPIC model for analyses of climate change scenarios. The ACS study consisted of three levels of agricultural inputs [organic (ORG), reduced (RED), and high (HI)] and three levels of cropping diversity [low (LOW), diversified annual grains (DAG), and diversified annuals and perennials (DAP)]. Changes in future SOC and MR under climate change were explored with ANOVA and recursive partitioning in multivariate analyses of inputs, diversity, growing season precipitation (GSP), growing degree days (GDD), annual average maximum, minimum temperatures, cumulative annual precipitation, and terrain attributes (TA). Under climate change, SOC decreased by 1.3% (from 132.3 to 130.6 Mg ha−1) of original stocks in the 0–90 cm. Microbial respiration was affected by climate change and increased by 17% (from 1.92 to 2.25 Mg C ha−1 y−1) due to an increase in annual maximum and minimum temperatures. The increase in annual maximum and minimum temperature was correlated with 32 and 42% of variation in SOC respectively. Monthly growing season GDD was correlated with 14% of variation of SOC in an analysis independent of annual data. Annual precipitation did not affect SOC, though May GSP accounted for 16% of total variation in SOC, while June temperature accounted for 9% of variation in MR. The combination of input and diversity was correlated with 3 and 7% of variation in SOC and MR, respectively. The combination of RED inputs and DAG diversity are the best option to manage SOC and reduce the amount of soil C respired under climate change relative to organic and conventional systems.

Projected monthly temperature changes of the Great Lakes Basin

The Great Lakes Basin is an important agricultural region for both the United States and Canada. The regional crop growths are affected by inter-annual climatic conditions and intra-seasonal variability. Consequently, monthly climate change projection data can provide more useful information for crop management than seasonal climate projections. However, very few studies undertaken for the Great Lakes Basin have focused on monthly timescales. In this study, we investigate the projected mid-century (2030–2059) monthly mean maximum temperature (Tmax) and minimum temperature changes of this region, relative to the baseline period (1980–2009). Future Tmax increases in this region are likely to be greater during the May to October period (coinciding with the region's growing season) than in other months. The order of magnitude of future Tmax and Tmin changes of the five Great Lakes sub-basins are Superior > Huron > Michigan > Erie and Ontario. Most future Tmax changes over land areas are higher than those over the lakes, whereas Tmin changes are likely to be higher over lakes than over the adjacent land areas in this region. The future number of extreme warm days (Tmax ≥ 29–32 °C) in this region will increase by between about 5 days (in the north) to 40 days (in southern parts of the basin), while the number of winter cold days (Tmax ≤ −5 °C ~ 0 °C) may decrease by between 3 days (south) and 35 days (north). This study furthermore identifies some fluctuations of latitudinal temperature gradients in the Great Lakes Basin, these areas covering the north latitude 40.5–41.5°, 43.5–44.0°, 45.5–46.5°, and 47.5–49.5°.

Fruit, mesocarp, and endocarp responses to crop load and to different estimates of source: sink ratio in olive (cv. Arauco) at final harvest

Abstract The annual fluctuations in olive crop load due to alternate bearing and other factors often lead to large differences in fruit size and oil content between years at harvest. A better understanding of how fruit parameters respond to the different leaf: fruit (i.e., source: sink) ratios that occur with contrasting crop loads would provide important information for crop management. Thus, the primary objectives of this study conducted with the cv. Arauco in three growing seasons were to: 1) determine the weight and size responses of the fruit and its main tissues, mesocarp (pulp) and endocarp (pit), to crop load; and 2) obtain relationships between different estimates of the source: sink ratio versus various fruit and oil parameters. Fruit thinning was performed by hand on uniform trees with high initial crop loads four weeks after full bloom the first season to obtain different crop loads at harvest. The thinning percentages the first season were 24%, 48% and 87%, along with an unthinned control. The same trees were then monitored the following two seasons without any further thinning. Fruit were sampled at harvest each season to determine fruit and tissue weights and diameters, oil weight per fruit, and oil concentration (%). Fruit weight was reduced 30–40% by high crop loads in each growing season with the mesocarp being much more affected than the endocarp. Oil weight per fruit (−50%) showed a somewhat greater reduction than fruit weight to crop load due to both fruit diameters and fruit oil concentration being decreased at high crop loads. Fruit and tissue weights and oil weight per fruit all displayed bilinear functions versus source: sink ratio when the source was expressed as canopy volume (a surrogate for leaf area) and sink on both a fruit number and glucose equivalent (GE) basis. Source limited fruit growth at both medium and high crop loads due to limited photoassimilate availability based on the bilinear functions, and the slope of the endocarp response to source: sink ratio was 15 times less than that of the mesocarp when expressed on a GE basis. A quantitative comparison with previously published studies indicated that maximum fruit weight appears to be obtained in olive between 1–2 m2 of leaf area per kg of GE. The bilinear relationships of source: sink ratio versus fruit weight observed in this study could contribute to crop modelling, and further research concerning how and when the mesocarp and endocarp respond to crop load is needed to aid crop management in obtaining sufficient fruit size and quality for table olive cultivars.

UNMANNED AERIAL VEHICLES FOR PESTICIDES AND FERTILIZERS APPLICATION IN PRECISION FARMING SYSTEM

The development of precision farming technology requires a higher level of technical support based on programmable, fully autonomous or remotely controlled unmanned aerial systems that contain complexes of automatic or remotely controlled unmanned aerial vehicles (UAVs). The authors conducted selection and justification of the main parameters of the UAV for agricultural purposes. For variable rate application of plant protection products and fertilizers the agrarians prefer UAVs of reusable, non-aerodrome basing, with small take-off for takeoff or vertical take-off and vertical landing, low-altitude, helicopter, rotary-wing and multi-rotor types equipped with an autopilot and a system for differentiating the distribution of fertilizer working fluids and pesticides for a set program, developed in accordance with the agrochemical cartogram and field phytosanitary condition. The equation of mass balance of UAV of agricultural purpose is made, which allows more precisely to determine the take-off weight taking into account the constituent masses of the payload intended for differentiated dispensing and distribution of working fluids of fertilizers and pesticides. The equation of UAV existence is obtained, which differs in that, in addition to the mass and flight-technical characteristics of the aircraft, it displays the technological parameters of differentiated application of pesticides and fertilizers for a particular field being treated. The technological process of UAV application in the system of precise farming includes sequential interrelated operations: monitoring and sounding of crops (using light UAVs equipped with multispectral cameras), obtaining, processing and transmitting information for crop management, differentiated application of pesticides and fertilizers according to a specified treatment program (UAV through a large payload).

Agricultural Soil Alkalinity and Salinity Modeling in the Cropping Season in a Spectral Endmember Space of TM in Temperate Drylands, Minqin, China

This paper presents the potential of the four-image spectral endmember (EM) space comprising sand (SL), green vegetation (GV), saline land (SA), and dark materials (DA), unmixed from Landsat TM/ETM+ to map dryland agricultural soil alkalinity and salinity (i.e., soil alkalinity (pH) and soil electrical conductivity (EC)) in the shallow root zone (0–20 cm) using partial least squares regression (PLSR) and an artificial neural network (ANN). The results reveal that SA, SL, and GV fractions at the subpixel level, and land surface temperature (LST) are necessary independent variables for soil EC modeling in Minqin Oasis, a temperate-arid system in China. The R2 (coefficient of determination) of the optimized parameters with the ANN model was 0.79, the root mean squared error (RMSE) was 0.13, and the ratio of prediction to deviation (RPD) was 1.95 when evaluated against all sampled data. In addition to the aforementioned four variables, the DA fraction and the recent historical SA fraction (SAH) in the spring dry season in 2008 were also helpful for soil pH modeling. The model performance is R2 = 0.76, RMSE = 0.24, and RPD = 1.96 for all sampled data. In summary, the stable EMs and LST space of TM imagery with an ANN approach can generate near-real-time regional soil alkalinity and salinity estimations in the cropping period. This is the case even in the critical agronomic range (EC of 0–20 dS·m−1 and pH of 7–9) at which researchers and policy-makers require near-real-time crop management information.

A hybrid approach for detecting corn and soybean phenology with time-series MODIS data

Abstract Monitoring crop phenology provides essential information for crop management, as well as for understanding regional to global scale vegetation dynamics. In this study, a hybrid phenology detection method is presented that incorporates the “shape-model fitting” concept of the two-step filtering method and a simulation concept of the crop models to detect the critical vegetative stages and reproductive stages of corn (Zea mays L.) and soybeans (Glycine max L.) from MODIS 250-m Wide Dynamic Range Vegetation Index (WDRVI) time-series data and 1000-m Land Surface Temperature (LST) data. The method was first developed and tested at the field scale over a ten-year period (2003–2012) for three experimental study sites in eastern Nebraska of USA, where the estimated phenology dates were compared to the ground-based phenology observations for both corn and soybeans. The average root mean square error (RMSE) of phenology stage estimation of the individual development stages across all sites ranged from 1.9 to 4.3 days for corn and from 1.9 to 4.9 days for soybeans. The approach was then tested at a regional scale over eastern Nebraska and the state of Iowa to evaluate its ability to characterize the spatio-temporal variation of targeted corn and soybean phenology stage dates over a larger area. Quantitative regional assessments were conducted by comparing the estimated crop stage dates with crop developmental stage statistics reported by the USDA NASS Crop Progress Reports (NASS-CPR) for both eastern Nebraska and Iowa. The accuracy of the regional-scale phenology estimation in Iowa (RMSE ranged from 2.6 to 3.9 days for corn and from 3.2 to 3.9 days for soybeans) was slightly lower than in eastern Nebraska (RMSE ranged from 1.8 to 2.9 days for corn and from 1.7 to 2.9 days for soybeans), However, the estimation accuracy in Iowa was still reasonable with the estimated phenology dates being within 4 days or less of the observed dates for both corn and soybeans.

Plant chlorophyll fluorescence: active and passive measurements at canopy and leaf scales with different nitrogen treatments.

Most studies assessing chlorophyll fluorescence (ChlF) have examined leaf responses to environmental stress conditions using active techniques. Alternatively, passive techniques are able to measure ChlF at both leaf and canopy scales. However, the measurement principles of both techniques are different, and only a few datasets concerning the relationships between them are reported in the literature. In this study, we investigated the potential for interchanging ChlF measurements using active techniques with passive measurements at different temporal and spatial scales. The ultimate objective was to determine the limits within which active and passive techniques are comparable. The results presented in this study showed that active and passive measurements were highly correlated over the growing season across nitrogen treatments at both canopy and leaf-average scale. At the single-leaf scale, the seasonal relation between techniques was weaker, but still significant. The variability within single-leaf measurements was largely related to leaf heterogeneity associated with variations in CO2 assimilation and stomatal conductance, and less so to variations in leaf chlorophyll content, leaf size or measurement inputs (e.g. light reflected and emitted by the leaf and illumination conditions and leaf spectrum). This uncertainty was exacerbated when single-leaf analysis was limited to a particular day rather than the entire season. We concluded that daily measurements of active and passive ChlF at the single-leaf scale are not comparable. However, canopy and leaf-average active measurements can be used to better understand the daily and seasonal behaviour of passive ChlF measurements. In turn, this can be used to better estimate plant photosynthetic capacity and therefore to provide improved information for crop management.

Determining the leaf emissivity of three crops by infrared thermometry.

Plant temperature can provide important physiological information for crop management. Non-contact measurement with an infrared thermometer is useful for detecting leaf temperatures. In this study, a novel technique was developed to measure leaf emissivity using an infrared thermometer with an infrared sensor and a thermocouple wire. The measured values were transformed into true temperatures by calibration equations to improve the measurement accuracy. The relationship between two kinds of measurement temperatures and setting emissivities was derived as a model for calculating of true emissivity. The emissivities of leaves of three crops were calculated by the mathematical equation developed in this study. The mean emissivities were 0.9809, 0.9783, 0.981 and 0.9848 for Phalaenopsis mature and new leaves and Paphiopedilum and Malabar chestnut leaves, respectively. Emissivity differed significantly between leaves of Malabar chestnut and the two orchids. The range of emissivities determined in this study was similar to that in the literature. The precision of the measurement is acceptable. The method developed in this study is a real-time, in situ technique and could be used for agricultural and forestry plants.

Regional scale cropland carbon budgets: Evaluating a geospatial agricultural modeling system using inventory data

Accurate quantification and clear understanding of regional scale cropland carbon (C) cycling is critical for designing effective policies and management practices that can contribute toward stabilizing atmospheric CO2 concentrations. However, extrapolating site-scale observations to regional scales represents a major challenge confronting the agricultural modeling community. This study introduces a novel geospatial agricultural modeling system (GAMS) exploring the integration of the mechanistic Environmental Policy Integrated Climate model, spatially-resolved data, surveyed management data, and supercomputing functions for cropland C budgets estimates. This modeling system creates spatially-explicit modeling units at a spatial resolution consistent with remotely-sensed crop identification and assigns cropping systems to each of them by geo-referencing surveyed crop management information at the county or state level. A parallel computing algorithm was also developed to facilitate the computationally intensive model runs and output post-processing and visualization. We evaluated GAMS against National Agricultural Statistics Service (NASS) reported crop yields and inventory estimated county-scale cropland C budgets averaged over 2000–2008. We observed good overall agreement, with spatial correlation of 0.89, 0.90, 0.41, and 0.87, for crop yields, Net Primary Production (NPP), Soil Organic C (SOC) change, and Net Ecosystem Exchange (NEE), respectively. However, we also detected notable differences in the magnitude of NPP and NEE, as well as in the spatial pattern of SOC change. By performing crop-specific annual comparisons, we discuss possible explanations for the discrepancies between GAMS and the inventory method, such as data requirements, representation of agroecosystem processes, completeness and accuracy of crop management data, and accuracy of crop area representation. Based on these analyses, we further discuss strategies to improve GAMS by updating input data and by designing more efficient parallel computing capability to quantitatively assess errors associated with the simulation of C budget components. The modularized design of the GAMS makes it flexible to be updated and adapted for different agricultural models so long as they require similar input data, and to be linked with socio-economic models to understand the effectiveness and implications of diverse C management practices and policies.