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Corn (Zea mays L.) is a major irrigated crop in the Texas High Plains (THP). Inadequate precipitation, declining groundwater resources, and inevitable seasonal drought in the area have led to a greater number of studies on crop hybrids, water regimes, and other yield-enhancing techniques. A four-year field study was conducted to investigate the effect of foliar fungicide timing on corn grain yield, grain moisture, and grain test weight under different irrigation regimes. A fungicide containing QoI + DMI was applied to four to six corn hybrids each year at various growth stages (V5, R1, and V5 + R1). For all years, corn plants were grown under three irrigation regimes to meet the seasonal evapotranspiration (ET) requirements of 50% (I50), 75% (I75), and 100% (I100). Hybrid response to the timing of fungicide application and irrigation regime was not consistent over the years. However, in 2 of the 4 years, timing of fungicide application resulted in yield differences among the hybrids, especially at I75 and L100. For those years, the fungicide applied at R1 resulted in a higher grain yield, grain moisture, and grain test weight followed by the fungicide applied at V5 + R1. Results showed that hybrids reacted differently to the foliar fungicide based on the availability of water under disease-free conditions. This study further revealed that irrigation water could be reduced from I100 to I75 with a limited (5.6% in 2011, 11.3% in 2012, 1.3% in 2013, and 0.3% in 2014) yield penalty in the THP region. [ABSTRACT FROM AUTHOR]

Red-soil sloping farmland in southern China plays a crucial role in the local economy and food production. However, improper tillage practices have resulted in topsoil degradation and deteriorating soil quality. This study investigated changes in soil physico-chemical properties under four tillage methods—cross-slope ridge tillage (RT), down-slope ridge tillage (DT), plastic mulching (PM), and conventional tillage (CT)—on red-soil sloping farmland. The study applied the Soil Management Assessment Framework (SMAF) to assess the influence of these tillage practices on soil quality. Results indicated that PM can increase the total porosity of the soil, reduce soil bulk density, and simultaneously decrease soil surface-water evaporation, significantly improving the soil's water-retention capacity. RT improved soil aggregate formation and stability, leading to increased macro-aggregate content, mean weight diameter, and soil water-stable aggregate stability rates. PM and RT effectively preserved soil nutrients like total nitrogen and organic matter, although PM lowered soil pH, potentially causing acidification. RT demonstrated the highest soil quality, with PM following. Crop growth positively impacted soil macro-aggregate content and stability, showing continuous improvement in soil structure and quality (p < 0.05). Priority should be given to RT in red-soil sloping farmland, followed by PM and CT, while avoiding DT if possible. This research furnishes valuable scientific substantiation for the selection of optimal tillage practices in the preservation of soil quality on red-soil slopes. [ABSTRACT FROM AUTHOR]

Purpose: Proper tillage management and crop plantation are two established approaches to control soil erosion in red soil slopes of southern China. This study aims to investigate the surface–subsurface runoff and soil losses processes on sloping farmland under different peanut growth stages and ridge treatments, and to quantify the combined effects of proper tillage management and crop plantation. Methods: Simulated rainfall experiments with an intensity of 60 mm h−1 were conducted to measure the initiation time and steady rates of surface–subsurface runoff and soil losses under four tillage treatments including flat planting (FP), longitudinal ridge (LR), cross ridge (CR), and alternate distribution of cross and longitudinal ridges (CL) at the bare land and four different growth stages of peanut. Results: As peanut grew, the mean steady surface runoff rate for all treatments decreased by 19.6%, 38.8%, 54.0%, and 66.3% at the four growth stages respectively, and the mean steady subsurface runoff rate increased by 35.4%, 77.3%, 99.9%, and 118.4% at the four growth stages respectively. The maximum soil loss rate also decreased from 7.33 ~ 25.92 g m−2 min−1 at the seedling stage to 0.31 ~ 4.72 g m−2 min−1 at the full fruit stage. Compared with the FP treatment, the steady surface runoff rate for the LR treatment increased by 8.1 ~ 32.4% and the steady subsurface runoff rate reduced by 7.2 ~ 38.4%; while for the CR treatment, the steady surface runoff rate reduced by 77.6 ~ 92.7% and the steady subsurface runoff rate increased by 26.6 ~ 219.3%. As peanut grew, the contribution index of peanut to surface runoff increased from 0 at bare land stage to 0.80, 0.96, and 0.34 for the LR, CL, and CR treatment at the S4 stage, while the contribution index of ridge tillage to runoff generally decreased. Conclusions: The inhibitory effects of peanut on surface runoff rate and sediment yield, as well as its promotion effect on subsurface runoff, were enhanced with peanut growth. Conversely, the impact of ridge tillage gradually decreased as peanut grew. On plots with longitudinal ridges, the impact of ridges on runoff was offset by contribution of crop growth after the seedling stage; whereas on plots with only contour ridges, the impact of ridges continued to dominate runoff control as peanut grew. [ABSTRACT FROM AUTHOR]

Groundwater contributes to the delivery of phreatic water to crop aeration zones via evapotranspiration, which is important for crop growth in drought-prone regions. Most studies on groundwater contribution have not considered the influence of crop growth stage or daily evapotranspiration. In this study, a neural network based on a genetic algorithm and the Levenberg–Marquardt backpropagation algorithm, as well as formula methods based on an accelerated genetic algorithm, were built to assess soybean groundwater contribution; in addition, a performance comparison was conducted. The results indicated that machine learning had the best performance for fitting errors, with values for relative mean error (RME), root mean square percentage error (RMSPE), and correlation coefficient of 1.088, 2.165, and 0.762, respectively; in addition, for validation errors, it had values for RME, RMSPE, and correlation coefficient of 1.069, 2.136, and 0.735, respectively. The machine learning method is recommended for readers seeking to calculate groundwater contribution. [ABSTRACT FROM AUTHOR]

Groundwater plays a key role in regulating the transition from meteorological droughts (MD) to agricultural droughts (AD), though its specific influence during crop growth remains unclear. In this study, daily effective root zone soil moisture and annual yield for winter wheat and summer maize were simulated using Aquacrop model under four scenarios: Scenario 1 (groundwater level at 1 m depth), Scenario 2 (groundwater level at 2 m depth), Scenario 3 (groundwater level at 3 m depth), and Scenario 4 (groundwater level at 4 m depth). Based on the simulated data, the propagation probabilities, propagation times (PT), translation rates of drought events, and changes in drought characteristics under the four scenarios were calculated, revealing the specific impact of groundwater levels on the propagation from MD to AD. The results indicated that (1) the probability of drought propagation increased with either a decrease in the groundwater level or an increase in the MD severity. Notably, a decrease in groundwater level from 1 m to 2 m depth results in a substantial rise in propagation probability, with an average increase of 114.0 % and 118.9 % for winter wheat and summer maize, respectively. (2) PT was shortened by 2–4 days for each 1 m drop in the groundwater level. When the severity of MD intensified from severe to extreme, the PT was shortened by an average of 8 days and 10 days during the phenological stages of summer maize and winter wheat, respectively. Both crops exhibited shorter PTs during the emergence, grouting, and maturity stages, and longer PTs during the tillering and heading stages. (3) Overall, the decline of groundwater levels accelerates the translation rate of drought events. In addition, longer and stronger drought characteristics were observed especially at low levels of groundwater. (4) The optimal groundwater level was found to be about 1 m and 2 m for winter wheat and summer maize in the Huaibei Plain, respectively. These findings provide valuable insights for enhancing drought resilience and optimizing irrigation and groundwater management in the Huaibei Plain. • Groundwater decline accelerates the drought propagation process. • Drought propagation time shortens by 2–4 days for each 1 m drop in groundwater level. • Crops are more sensitive to meteorological drought during critical growth stages. • Optimal groundwater depth is 1 m (wheat) and 2 m (maize), deeper levels reduce yield. [ABSTRACT FROM AUTHOR]

Lysimetric and eddy covariance techniques are commonly used to directly estimate actual crop evapotranspiration (ETa). However, these technologies are costly, laborious, and require skills which make in situ ET estimation difficult, particularly in developing countries. With this in mind, an attempt was made to determine ETa and stagewise crop coefficient (Kc) values of transplanted puddled rice using a modified non-weighing paddy lysimeter. The results were compared to indirect methods, viz., FAO Penman–Monteith and pan evaporation. Daily ETa ranged from 1.9 to 8.2 mmday−1, with a mean of 4.02 ± 1.35 mmday−1, and their comparison showed that the FAO Penman–Monteith equation performed well for the coefficient of determination (R2 of 0.63), root mean squared error (RMSE = 0.80), and mean absolute percentage error (MAPE = 13.6 %), and was highly correlated with ETa throughout the crop season. However, the pan evaporation approach was underestimated (R2 of 0.24; RMSE = 0.98; MAPE = 22.13%) due to a consistent pan coefficient value (0.71), vegetation role and measurement errors. In addition, actual Kc values were obtained as 1.13 ± 0.13, 1.27 ± 0.2, 1.23 ± 0.16, and 0.93 ± 0.18 for the initial, crop development, mid-season, and end-season stages, respectively. These estimated crop coefficient values were higher than FAO Kc values. Statistical analysis results revealed that the overall stagewise-derived average Kc values were in line with FAO values, but different from the derived pan Kc values, although found insignificant at a 5% significance level. In addition, water productivity and agro-meteorological indices were derived to evaluate the cultivar performance in this experiment. Therefore, such a methodology may be used in the absence of weighing lysimeter-derived Kc values. The derived regional Kc values can be applied to improve irrigation scheduling under similar agro-climatic conditions.

Groundwater contributes to the delivery of phreatic water to crop aeration zones via evapotranspiration, which is important for crop growth in drought-prone regions. Most studies on groundwater contribution have not considered the influence of crop growth stage or daily evapotranspiration. In this study, a neural network based on a genetic algorithm and the Levenberg–Marquardt backpropagation algorithm, as well as formula methods based on an accelerated genetic algorithm, were built to assess soybean groundwater contribution; in addition, a performance comparison was conducted. The results indicated that machine learning had the best performance for fitting errors, with values for relative mean error (RME), root mean square percentage error (RMSPE), and correlation coefficient of 1.088, 2.165, and 0.762, respectively; in addition, for validation errors, it had values for RME, RMSPE, and correlation coefficient of 1.069, 2.136, and 0.735, respectively. The machine learning method is recommended for readers seeking to calculate groundwater contribution.

Quantification of grain yield losses in mega rice varieties of south Asia due to infection by the sheath blight pathogen (Rhizoctonia solani) at different growth stages was carried out in 2 years in Northern India. Pathogen inoculation was done at the tillering, booting and grain‐filling stages of the crop under field conditions. The disease variables viz. disease severity, disease incidence, flag leaf infection and relative yield loss were determined on the mega varieties viz Swarna, Swarna Sub‐1 and PR 122. The effect of crop growth stage at inoculation and varieties was more important for disease variables than for relative yield loss. Booting and tillering stages were the most critical for higher sheath blight development with the highest relative yield loss. Among the mega rice varieties Swarna and Swarna‐Sub1, no significant difference was observed in all the disease variables and grain yield loss while the local variety PR 122 outperformed both mega varieties in disease variables and grain yield loss. PR 122 had the lowest yield losses due to low disease severity (14.22%–19.23%) during both years. All disease variables were positively correlated with relative yield losses. Regarding disease management strategies the focus should be on managing the disease initiation, particularly at the tillering and booting stages of the crop. [ABSTRACT FROM AUTHOR]

The normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI) derived from Moderate Resolution Imaging Spectroradiometer (MODIS) satellite imagery are widely used for crop yield analysis. However, the growth metrics derived from the MODIS NDVI or EVI have so far not been explored and applied to crop yield yet. To the best of our knowledge, this study is the first to design NDVI- and EVI-based crop growth metrics, which biometrically capture the status and trend of crop growth and thus could be more powerful for growth yield management. We developed 19 NDVI- and EVI-based growth metrics, respectively, to monitor crop growth and yield, which is based on a time series of MODIS Terra 16-day 250 m data product from 2000 to 2018. Among the NDVI- and EVI-based vegetation growth metrics (VGM), the maximum (VGMmax), the integrated (VGMinteg), the sum of green-up (VGMsumgrn), the 70 days growth stage (VGM70), 85 days growth stage (VGM85), and 98 days growth stage (VGM98), the sum of 85 days growth stage (VGM85total), and the sum of 98 days growth stage (VGM98total) are mentionable. In this study, we implemented these crop growth metrics for soybean crop yield modeling at Mississippi Delta, Mississippi, USA. Soybean is a major crop cultivated in this region that is consisted of a total of 18 counties with similar agricultural cropping patterns. We observed that NDVI- and EVI-based VGMmax, VGM70, VGM85, VGM98total fitted models best with R-Square about 0.95. Using cross-validation of 80% train and 20% test size, we found NDVI-based VGM85 (e.g., normalized mean prediction error (NMPE) = 0.034) and EVI-based VGMmax (NMPE = 0.033) were the best fit linear yield models for this region. Designing novel crop growth indices based on crop phenological and ecological characteristics, this study further showed NDVI- and EVI-based growth metrics for crop growth monitoring and yield modeling. These growth metrics can be applied to other types of crop monitoring in different climate zones.

The accurate and timely estimates of crop physiological growth stages are essential for efficient crop management and precise modeling of agricultural systems. Satellite remote sensing has been widely used to retrieve vegetation phenology metrics at local to global scales. However, most of these phenology metrics (e.g., green-up) are different from crop growth stages (e.g., emergence) used in crop management and modeling. As such, an integrated framework referred to as PhenoCrop was developed to: 1) establish a connection between remote sensing-derived phenology metrics and key crop growth stages based on Wang and Engle plant phenology model and 2) use fused MODIS-Landsat 30 m 8-day reflectance data generated using Kalman Filter-based data fusion technique to produce onset dates of key growth stages of corn (Zea mays L.) and soybeans (Glycine max L.) at 30 m spatial resolution. In this paper, we described the PhenoCrop framework, and tested its performance for the State of Nebraska for 2012–2016 by comparison to observations of estimated key growth stages at four experimental sites, and state-level statistical data from Crop Progress Reports (CPRs) published by the United States Department of Agriculture’s (USDA) National Agricultural Statistical Services (NASS). In addition, to evaluate the suitability of using coarse or high spatial resolution satellite imagery, fused MODIS-Landsat-based estimates were compared with those produced using EOS MODIS 250 m (MOD9Q1) reflectance data.The PhenoCrop estimates captured the typical spatial trends of gradual delay in the progression of the growing season from southeast to northwest Nebraska. Also inter-annual differences due to factors such as weather fluctuations and change in management strategies (e.g., early season in 2012) were evident in the estimates. Validation results revealed that average root mean square error (RMSE) of the state-level estimates of corn and soybean growth stages ranged from 1.10 to 4.20 days and from 3.81 to 7.89 days, respectively, while pixel level estimates had a RMSE ranging from 3.72 to 8.51 days for corn and 4.76–9.51 days for soybean growth stages. Although MODIS 250 m based estimates showed similar general spatial patterns observed in the fused MODIS-Landsat based estimates, the accuracy and ability to capture field scale variations was improved with fused MODIS-Landsat data. Overall, results showed the ability of PhenoCrop framework to provide reliable estimates of crop growth stages that can be highly useful in crop modeling and crop management during the growing season.

Crop emergence is a critical stage for crop development modeling, crop condition monitoring, and biomass accumulation estimation. Green-up dates (or the start of the season) detected from remote sensing time series are related to, but generally lag, crop emergence dates. In this paper, we refine the within-season emergence (WISE) algorithm and extend application to five Corn Belt states (Iowa, Illinois, Indiana, Minnesota, and Nebraska) using routine harmonized Landsat and Sentinel-2 (HLS) data from 2018 to 2020. Green-up dates detected from the HLS time series were assessed using field observations and near-surface measurements from PhenoCams. Statistical descriptions of green-up dates for corn and soybeans were generated and compared to county-level planting dates and district- to state-level crop emergence dates reported by the National Agricultural Statistics Service (NASS). Results show that emergence dates for corn and soybean can be reliably detected within the season using the HLS time series acquired during the early growing season. Compared to observed crop emergence dates, green-up dates from HLS using WISE were ~3 days later at the field scale (30-m). The mean absolute difference (MAD) was ~7 days and the root mean square error (RMSE) was ~9 days. At the state level, the mean differences between median HLS green-up date and median crop emergence date were within 2 days for 2018–2020. At this scale, MAD was within 4 days, and RMSE was less than 5 days for both corn and soybeans. The R-squares were 0.73 and 0.87 for corn and soybean, respectively. The 2019 late emergence of crops in Corn Belt states (1–4 weeks to five-year average) was captured by HLS green-up date retrievals. This study demonstrates that routine within-season mapping of crop emergence/green-up at the field scale is practicable over large regions using operational satellite data. The green-up map derived from HLS during the growing season provides valuable information on spatial and temporal variability in crop emergence that can be used for crop monitoring and refining agricultural statistics used in broad-scale modeling efforts.

Watermelon has been reported to be vulnerable to insect pest pressure throughout its growth stages with a resultant indiscriminate calendar-based insecticide application (of up to 25 sprays/season in the study area), with its attendant consequences. In order to recommend the crop growth period(s) to effect chemical control measures that will give better return on investment, field trials were set-up in the early- and late-cropping seasons of 2016 and 2017. Forty, 5m long x 8m wide plots were demarcated in randomized complete block design in 4 replications. Treatments were applications of 0.5% Cyper-diforce® (Cypermethrin 30g/L + Dimethoate 250g/L) at seedling, mid-vegetative, mid-flowering, mid-fruiting stages and their combinations. Unsprayed plots served as control. Pest and beneficial arthropod density, leaf injury, crop growth and yield data were collected and subjected to variance analysis and significantly different means were separated by SNK at 5% level of probability. Cost:benefit ratios of the various treatments was also computed. Findings indicate that, leaf-eating beetles had the highest impact on stand survival and yield. Insect pest density and damage generally decreased with increase in frequency of insecticide application and treatment differences were significant (p< 0.05) except in the case of A. gossypii in the late-sown crop of 2017. Plot sprayed at seedling + vegetative + fruiting stages gave the highest fruit yield and return on investment. It is therefore suggested that, for economic production of watermelon, the crop should not be sprayed during the flowering period. [ABSTRACT FROM AUTHOR]

Water stress and sensitive coefficient of crops are important parameters, indicating the effect of water regime on productivity. They provide a scientific basis for management agencies and farmers to manage and operate their irrigation systems. A field experiment was carried out on an area of 450m2, divided into 42 experimental plots with different cultivation methods (conventional and SRI) and under different water regimes. Measurement parameters of each plot included irrigation water, rainfall, in-flow, evapotranspiration, capillary water going up while the surface soil layer was revealing and drying, in/out underground water flows in cultivating soil layer, infiltration water, difference of water levels on the experimental plots at the beginning and end of the study. Evapotranspiration in each growth stages of rice crops and in each experimental plot was determined. Then Jensen's yield prediction model was calibrated to predict rice yield as affected by the water stress. The result revealed that if water shortage had occurred, the yield of spring rice which was cultivated following SRI methods would be less than that cultivated by conventional method. Moreover, the effects of water shortage in 1st and 2nd growth stages of conventional cultivated summer rice were less than that cultivated following SRI method in the same stages. The yield of rice crops cultivated following the SRI method was less affected by the lack of water in 3rd and 4th growth stages, as compared to that cultivated by conventional method. [ABSTRACT FROM AUTHOR]

Abstract: A better understanding of recent crop yield trends is necessary for improving the yield and maintaining food security. Several possible mechanisms have been investigated recently in order to explain the steady growth in maize yield over the US Corn‐Belt, but a substantial fraction of the increasing trend remains elusive. In this study, trends in grain filling period (GFP) were identified and their relations with maize yield increase were further analyzed. Using satellite data from 2000 to 2015, an average lengthening of GFP of 0.37 days per year was found over the region, which probably results from variety renewal. Statistical analysis suggests that longer GFP accounted for roughly one‐quarter (23%) of the yield increase trend by promoting kernel dry matter accumulation, yet had less yield benefit in hotter counties. Both official survey data and crop model simulations estimated a similar contribution of GFP trend to yield. If growing degree days that determines the GFP continues to prolong at the current rate for the next 50 years, yield reduction will be lessened with 25% and 18% longer GFP under Representative Concentration Pathway 2.6 (RCP 2.6) and RCP 6.0, respectively. However, this level of progress is insufficient to offset yield losses in future climates, because drought and heat stress during the GFP will become more prevalent and severe. This study highlights the need to devise multiple effective adaptation strategies to withstand the upcoming challenges in food security. [ABSTRACT FROM AUTHOR]

The rainfed cropland belt in Australia is of great importance to the world grain market but has the highest climate variability of all such regions globally. However, the spatial-temporal impacts of climate variability on crops during different crop growth stages across broadacre farming systems are largely unknown. This study aims to quantify the contributions of climate and Land Surface Temperature (LST) variations to the variability of the Enhanced Vegetation Index (EVI) by using remote sensing methods. The datasets were analyzed at an 8-day time-scale across the rainfed cropland of eastern Australia. First, we found that EVI values were more variable during the crop reproductive growth stages than at any other crop life stage within a calendar year, but nevertheless had the highest correlation with crop grain yield (t ha −1 ). Second, climate factors and LST during the crop reproductive growth stages showed the largest variability and followed a typical east-west gradient of rainfall and a north-south temperature gradient across the study area during the crop growing season. Last, we identified two critical 8-day periods, beginning on day of the year (DoY) 257 and 289, as the key ‘windows’ of crop growth variation that arose from the variability in climate and LST. Our results show that the sum of the variability of the climate components within these two 8-day ‘windows’ explained >88% of the variability in the EVI, with LST being the dominant factor. This study offers a fresh understanding of the spatial-temporal climate-crop relationships in rainfed cropland and can serve as an early warning system for agricultural adaptation in broadacre rainfed cropping practices in Australia and worldwide. [ABSTRACT FROM AUTHOR]

An improved understanding of temporal and spatial variations in soil respiration is essential for measuring soil CO2 effluxes accurately. In this study, a field experiment was conducted to investigate the spatial and temporal variability of soil respiration between adjacent crop rows in a soybean (Glycine max L.) field. Soil CO2 concentration, water content, and temperature at a 7.5 cm depth were recorded continuously at 0 cm, 12 cm, 24 cm, and 35 cm from the plant row during the growing season. Root biomass at the corresponding locations was collected from the 0 to 10 cm and 10 to 20 cm soil layers at three growth stages. Seasonal CO2 efflux data showed that the minimum value appeared at the seeding stage, increased gradually, reached the maximum at the flowering and grain-filling stages, and then dropped steadily at the mature stage. Within a growth stage, CO2 effluxes related positively to soil temperature, but negatively to soil water content. In the early and vigorous growing stages of soybean crop, soil respiration showed apparent diurnal variations, and was most significant at the crop row location. Except for the seeding stage, CO2 effluxes at the crop row were larger than that of other locations, and effluxes at 35 cm from the row were representative of the mean CO2 efflux between adjacent rows. We concluded that the spatial heterogeneity of CO2 efflux between crop rows should be taken into consideration when measuring soil respiration in agricultural ecosystems. [ABSTRACT FROM AUTHOR]

The ability to regionally monitor crop progress and condition through the growing season benefits both crop management and yield estimation. In the United States, these metrics are reported weekly at state or district (multiple counties) levels by the U.S. Department of Agriculture (USDA) National Agricultural Statistics Service (NASS) using field observations provided by trained local reporters. However, the ground data collection process supporting this effort is time consuming and subjective. Furthermore, operational crop management and yield estimation efforts require information with more granularity than at the state or district level. This paper evaluates remote sensing approaches for mapping crop phenology using vegetation index time-series generated by fusing Landsat and MODIS (Moderate Resolution Imaging Spectroradiometer) surface reflectance imagery to improve temporal sampling over that provided by Landsat alone. The case study focuses on an agricultural region in central Iowa from 2001 to 2014. Our objectives are 1) to assess Landsat-MODIS data fusion results over cropland; 2) to map crop phenology at 30 m resolution using fused surface reflectance data; and 3) to identify the relationships between remotely sensed crop phenology metrics and the crop progress stages reported by NASS. The results show that detailed spatial and temporal variability in vegetation development across this landscape can be identified using the fused Landsat-MODIS data. The mean difference (bias) in Normalized Difference Vegetation Index (NDVI) between actual Landsat observations and the fused Landsat-MODIS data, generated for Landsat overpass dates, is in the range of − 0.011 to 0.028 for every year. The derived phenological metrics show distinct features for different crops and natural vegetation at field scales. Strong correlations are observed between remotely sensed phenological stages, based on NDVI curve inflection points, and the observed crop physiological growth stages from the NASS Crop Progress (CP) reports. The green-up dates detected from remote sensing data typically occurred during crop vegetative stages when 2–4 leaves were developed for both corn and soybeans, or about 1–3 weeks after the reported emergence dates when the plant were first visible to ground-based observers. Despite being a lagging indicator, remotely sensed green-up can be used effectively to backcast emergence, e.g. as input to spatially distributed crop models. The differences in green-up date between corn and soybean were 8–10 days, consistent with the offset in emergence dates reported by NASS at district level. The reported harvest dates were typically about 2–3 weeks after the dormancy stage was detected via remote sensing for corn and about 1–2 weeks for soybeans. This suggests that probable harvest times for individual fields may be predicted 1–3 weeks ahead using remote sensing data. The results suggest that crop phenology and certain growth stages at field scales (30 m spatial resolution) can be linked and mapped by integrating imagery from multiple remote sensing platforms. [ABSTRACT FROM AUTHOR]

Dynamics of ammonia-oxidizing bacterial (AOB) and archaeal (AOA) abundance was assayed in a tropical Humic Nitisol during two cropping seasons of a long-term field experiment situated in the central highlands of Kenya. Since 2002, soils were treated yearly with biochemically contrasting organic inputs (4 Mg C ha year) of Tithonia diversifolia (TD; C/N ratio: 13; lignin: 8.9 %; polyphenols: 1.7 %), Calliandra calothyrsus (CC; 13; 13; 9.4) and Zea mays (ZM; 59; 5.4; 1.2) combined with and without 120 kg CaNHNO ha season. In 2012 and 2013, soils (0-15 cm) were sampled at young growth (EC30) and flowering (EC60) stages of maize and subjected to DNA-based amoA gene quantification. ZM and TD increased AOB abundance by 9 and 19 %, respectively, compared to CC, while AOA remained unaffected. This was ascribed to high organic N in TD and lower lignin and polyphenol contents in ZM than in CC. In CC, formation of polyphenol-protein complexes limited microbial access to N. Sole use of mineral N or its combination with organic inputs decreased AOA abundance by 35 % but not AOB as a consequence of pH reduction by mineral N. Overall, AOB was more responsive than AOA to input quality that became most pronounced under optimal soil moisture conditions found in 2013 and at EC60 in 2012. We recommend prolonged study periods, considering also rRNA-based analyses to explore the dynamics of active nitrifying communities as a consequence of interrelations of contrasting organic inputs, crop growth stages and seasonality in agricultural soils. [ABSTRACT FROM AUTHOR]

O nitrogênio é o nutriente exigido em maiores quantidades pela cultura do feijão. A resposta à sua aplicação depende da dose aplicada e da época de sua aplicação. O objetivo deste trabalho foi avaliar os componentes de produção, produtividade de grãos e a qualidade fisiológica de sementes de feijão, decorrentes de diferentes doses de N (uréia) aplicadas em cobertura em três estádios da cultura. O experimento foi conduzido no sistema plantio direto. Os tratamentos foram constituídos por 0, 40, 80, 120,160, 200 e 240 kg ha-1 de N aplicados em cobertura nos estádios V4-5, R5 e R6, correspondendo, respectivamente, a 21, 32 e 38 dias após a emergência das plantas. A qualidade fisiológica de sementes foi avaliada por meio do teste de germinação e testes de vigor. O N aplicado nas diferentes fases da cultura não interferiu nos componentes de produção. A máxima produtividade de grãos foi obtida com 164 kg ha-1 de N em cobertura, independentemente do estádio de desenvolvimento. A qualidade fisiológica das sementes não foi influenciada pelos tratamentos.The nitrogen is the nutrient required in greatest quantities by the bean crop and the response to its application depends on the applied N rate as well as the time of its application. The objective of this work was to evaluate yield components, grain productivity and physiological quality of bean seeds, affected by different nitrogen (urea) rates top-dressed at three crop growth stages. The experiment was conducted in no-till system. The treatments were constituted by 0, 40, 80, 120, 160, 200 and 240 kg ha-1 N top-dressed at V4-5, R5 and R6 growth stages, corresponding to 21, 32 and 38 days after plant emergence, respectively. Seed physiological quality was evaluated by germination and vigor tests. Nitrogen applied at different crop growth stages did not interfere on the yield components, but interfered on bean productivity. Maximum grain productivity was obtained with top-dressed 164 kg ha-1 N, independently of growth stage application. Seed physiological quality was not affected by the treatments, fitting in the category of seeds for commercialization.

• Soil detachment capacity and rill erodibility decreased during growing season. • Rill erodibility (K r) decreased as an exponential function of root density. • Soil detachment capacity was simulated by shear stress, soil cohesion, bulk density, and root length density. Near soil-surface characteristics change significantly with crop growth in sloping farmlands and thereby induce the temporal variations in soil detachment by rill flow. In this study, variations in soil detachment capacity by rill flow (Dc) and soil erosion resistance to rill flow as reflected by rill erodibility (K r) and critical shear stress (τ c) during crop growth stages were quantified in sloping farmlands of the Loess Plateau, China. The soil samples were collected from four different plots (one control and three crop plots—soybean, corn, and millet) and subjected to flume experiments under different shear stress (τ) levels (5.16–15.61 Pa). The near soil-surface characteristics including soil moisture content (SMC), soil bulk density (BD), soil cohesion (Coh), mean weight diameter (MWD), soil organic matter content (SOM), root length density (RLD), root surface area density (RSAD), and root mass density (RMD), were measured to explain the variations in soil detachment during crop growth stages. The results showed that SMC fluctuated greatly, whereas BD, Coh, MWD, SOM, RLD, RSAD, and RMD increased gradually with crop growth. Measured Dc and fitted K r significantly decreased with crop growth during one growing season. The mean values of Dc for soybean, corn, and millet decreased by 61.19–67.23%, K r decreased by 55.53–61.36%, and τ c increased by 16.0–18.31% compared to those for the bare soil control. The decrease in Dc and K r were significantly affected by BD, Coh, SOM, MWD and RLD, RSAD, and RMD (p < 0.01). Seasonal variations in Dc were well simulated by τ , BD, Coh, and RLD (R 2 = 0.96, NSE = 0.96). The findings demonstrate that it is essential to consider the seasonal variations in soil detachment for optimizing soil and water conservation measures in sloping farmlands. [ABSTRACT FROM AUTHOR]

Crop emergence is a critical stage for crop development modeling, crop condition monitoring, and biomass accumulation estimation. Green-up dates (or the start of the season) detected from remote sensing time series are related to, but generally lag, crop emergence dates. In this paper, we refine the within-season emergence (WISE) algorithm and extend application to five Corn Belt states (Iowa, Illinois, Indiana, Minnesota, and Nebraska) using routine harmonized Landsat and Sentinel-2 (HLS) data from 2018 to 2020. Green-up dates detected from the HLS time series were assessed using field observations and near-surface measurements from PhenoCams. Statistical descriptions of green-up dates for corn and soybeans were generated and compared to county-level planting dates and district- to state-level crop emergence dates reported by the National Agricultural Statistics Service (NASS). Results show that emergence dates for corn and soybean can be reliably detected within the season using the HLS time series acquired during the early growing season. Compared to observed crop emergence dates, green-up dates from HLS using WISE were ~3 days later at the field scale (30-m). The mean absolute difference (MAD) was ~7 days and the root mean square error (RMSE) was ~9 days. At the state level, the mean differences between median HLS green-up date and median crop emergence date were within 2 days for 2018–2020. At this scale, MAD was within 4 days, and RMSE was less than 5 days for both corn and soybeans. The R-squares were 0.73 and 0.87 for corn and soybean, respectively. The 2019 late emergence of crops in Corn Belt states (1–4 weeks to five-year average) was captured by HLS green-up date retrievals. This study demonstrates that routine within-season mapping of crop emergence/green-up at the field scale is practicable over large regions using operational satellite data. The green-up map derived from HLS during the growing season provides valuable information on spatial and temporal variability in crop emergence that can be used for crop monitoring and refining agricultural statistics used in broad-scale modeling efforts. [ABSTRACT FROM AUTHOR]

In this research, the performance of satellite rainfall estimates (SREs) for crop growth simulation was investigated. Rainfall products selected were CHIRPS 2.0, CMORPH 1.0, MSWEP 2.2, and RFE 2.0. In-situ rainfall from 20 stations within the Lake Victoria basin in Kenya served as reference. Rainfall products were evaluated for onset days, rainfall depths, dry spells, and rainfall occurrence for four crop growth stages. Assessment was on a daily time step for the period 2012–2018 and on a point-to-pixel basis. Results showed that SREs exhibit large variation in timing of rainfall arrival. SREs exhibited largest interannual and spatial spreads in representing dry spell length during the flowering stage with CMORPH and CHIRPS showing best and weakest results, respectively. Bias of SREs in representing dry spells was smaller during early growth stages. Detecting rainfall occurrence by the SREs weakened as the growing season progressed. MSWEP, followed by RFE2, produced the best results in detecting rainfall events, while falsely detected rainfall was frequent in CHIRPS, particularly in later growth stages. SREs generally performed better during a wet than a dry growing season. SREs indicated less bias in rainfall depths during the early stages of crop growth but deteriorated at later stages. MSWEP and CMORPH exhibited the least and highest interannual spread in relative bias, respectively. In associating biases to severe and extreme water stress, based on crop water requirement satisfaction index, effects were more prevailing in the ripening than flowering stages. Findings of this study suggest that SREs can serve as input to crop growth modelling, but validation of SREs with rain gauge observed counterparts is essential. • Timing of rainfall arrival by satellite rainfall products (SREs) widely varies. • SRE's bias in representing dry spells is smaller during early crop growth stages. • SREs perform better during a wet than a dry growing season. • Performance of SREs weakens as the crop growing season progresses. • SREs' error propagation in crop water stress is more prevailing in ripening that flowering stages. • SREs can serve as crop growth simulations input but validation is essential. [ABSTRACT FROM AUTHOR]

• Crop growth metrics are developed using time series NDVI and EVI data. • NDVI- and EVI-based growth metrics are analyzed for crop yield modeling. • The 70 and 85 days growth indices best predict soybean yield. • The maximum growth metrics (NDVI- and EVI- based) are other important indicators for soybean yield. The normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI) derived from Moderate Resolution Imaging Spectroradiometer (MODIS) satellite imagery are widely used for crop yield analysis. However, the growth metrics derived from the MODIS NDVI or EVI have so far not been explored and applied to crop yield yet. To the best of our knowledge, this study is the first to design NDVI- and EVI-based crop growth metrics, which biometrically capture the status and trend of crop growth and thus could be more powerful for growth yield management. We developed 19 NDVI- and EVI-based growth metrics, respectively, to monitor crop growth and yield, which is based on a time series of MODIS Terra 16-day 250 m data product from 2000 to 2018. Among the NDVI- and EVI-based vegetation growth metrics (VGM), the maximum (VGMmax), the integrated (VGMinteg), the sum of green-up (VGMsumgrn), the 70 days growth stage (VGM70), 85 days growth stage (VGM85), and 98 days growth stage (VGM98), the sum of 85 days growth stage (VGM85total), and the sum of 98 days growth stage (VGM98total) are mentionable. In this study, we implemented these crop growth metrics for soybean crop yield modeling at Mississippi Delta, Mississippi, USA. Soybean is a major crop cultivated in this region that is consisted of a total of 18 counties with similar agricultural cropping patterns. We observed that NDVI- and EVI-based VGMmax, VGM70, VGM85, VGM98total fitted models best with R-Square about 0.95. Using cross-validation of 80% train and 20% test size, we found NDVI-based VGM85 (e.g., normalized mean prediction error (NMPE) = 0.034) and EVI-based VGMmax (NMPE = 0.033) were the best fit linear yield models for this region. Designing novel crop growth indices based on crop phenological and ecological characteristics, this study further showed NDVI- and EVI-based growth metrics for crop growth monitoring and yield modeling. These growth metrics can be applied to other types of crop monitoring in different climate zones. [ABSTRACT FROM AUTHOR]

Ammonia oxidation, the rate-limiting step of autotrophic nitrification, is carried out by ammonia-oxidizing archaea (AOA) and bacteria (AOB) that differ in their physiology and metabolic pathways. However, there has been little field investigations on the dynamic responses of ammonia oxidizers to different fertilizer inputs in intensive crop rotations. Here, we examine the soil potential nitrification rates (PNR), abundance and communities of AOB and AOA across different growth stages of wheat and maize at two soil depths (0–20 and 20–40 cm) under three fertilization regimes, namely zero fertilization (CK), chemical fertilizer (NPK) and partial replacement of the chemical fertilizer nutrients with organic manure (NPKM). Soil PNR was higher in the surface soil than in the subsoil in the wheat season, but no significant difference was observed in the maize season. Fertilization especially NPK treatment increased soil PNR, and the effect was more pronounced in the surface soil in the wheat season. The influence of soil depth on the abundance and communities of AOA and AOB was greater than that of fertilizer regime or plant growth stage across wheat and maize growth stages. The differences of ammonia oxidizers communities were mainly attributable to soil total nitrogen, organic carbon and soil moisture contents in the two depths. The abundance of AOB decreased more sharply than that of AOA with increasing soil depth. In general, application of NPK only increased AOB abundance in both wheat and maize seasons, whereas NPKM treatment increased AOB abundance only in the maize season (except in the subsoil at harvest). Generally, soil PNR was correlated with AOB and AOA abundances except for AOB in the subsoil in the wheat season and AOA in the surface soil during maize growth. Fertilization regime did not increase AOA abundance or alter AOA communities across all soil depths and plant growth stages. Our results indicate that the ammonia oxidizers differ greatly in responses to fertilization regimes and the changes are associated with soil depth and crop growth stage. The dynamics of soil microorganisms should be integrated into fertilizer and crop management to achieve sustainable agricultural systems. Unlabelled Image • AOB abundance decreased more sharply than that of AOA with increasing soil depth. • Chemical fertilizer increased AOB abundance but had no effect on AOA. • The increase of AOB abundance in NPKM treatment was mainly in the maize season. • Relationships of AOA and AOB abundance and PNR are depend on soil depth and crops. [ABSTRACT FROM AUTHOR]

Crop emergence date is a critical input to models of crop development and biomass accumulation. The ability to robustly detect and map emergence date using remote sensing would greatly benefit operational yield estimation and crop monitoring efforts; however, this has proven to be challenging. Previous remote-sensing phenology algorithms showed that crop stages can typically be detected starting only around the V3-V4 (3 to 4 established leaves) vegetative stage. Furthermore, traditional approaches have a strong assumption regarding the temporal evolution of plant growth and normally require a complete growth period of observations to define seasonal changes. Most approaches were not designed for within-season operational mapping, particularly in the early growing season. In the current paper, we describe a new within-season emergence (WISE) approach to mapping crop green-up date using satellite observations available during early growth stages. The approach was first optimized using high spatiotemporal resolution (10 m, 2-day revisit) imagery from the Vegetation and Environment monitoring New MicroSatellite (VENμS) research mission, and assessed using ground observations of early crop growth stages (emergence VE and one leaf V1 stages for corn, and emergence VE and unifoliolate VC stages for soybeans) collected over the Beltsville Agricultural Research Center (BARC) experimental fields in Beltsville, MD during the 2019 growing season. Results show that early crop growth stages can be reliably detected at sub-field scale about two weeks after crop emergence. The remote-sensing green-up dates were about 4–5 days after crop emergence on average. Coefficients of determination (R2) between green-up dates and the mid-point dates of the early growth stages were above 0.90. The mean absolute differences, standard deviations, and root mean square errors comparing to the early growth stage mid-point dates were within six days. The maximum differences were within ±10 days across all fields. The WISE approach was assessed using operational Sentinel-2 data (10 m, 5-day revisit) over BARC. The detected green-up dates from Sentinel-2 were consistent with those from VENμS. Some fields were not detected due to the lack of observations around the emergence dates. For independent evaluation, the WISE approach was applied over an agricultural watershed on the Maryland Eastern Shore using both VENμS and the Harmonized Landsat and Sentinel-2 (HLS) data (30 m, 3–4-day revisit). The detected green-up dates were compared with emergence dates in crop progress reports from the National Agricultural Statistics Service (NASS) at the state-level. The WISE-detected green-up dates at the regional scale are within VE stage ranges but slightly earlier than NASS crop progress reports at the state-level. The WISE approach uses remote-sensing observations during the early crop growth stages and has potential for operational application within the season using Sentinel-2 and HLS data. • A robust approach detected crop emergence in sub-fields during early growing season. • VENμS time series revealed green-up events within two weeks after crop emergence. • Sentinel-2 produced similar results to VENμS when observations are frequent. • HLS could be used for mapping crop emergence operationally. [ABSTRACT FROM AUTHOR]