Should phenological information be applied to predict agronomic traits across growth stages of winter wheat?

Most existing agronomic trait models of winter wheat vary across growing seasons, and it is an open question whether a unified statistical model can be developed to predict agronomic traits using a vegetation index(VI) across multiple growing seasons. In this study, we constructed a hierarchical linear model(HLM) to automatically adapt the relationship between VIs and agronomic traits across growing seasons and tested the model’s performance by sensitivity analysis. Results demonstrated that(1) optical VIs give poor performance in predicting AGB and PNC across all growth stages, whereas VIs perform well for LAI, LGB, LNC, and SPAD.(2) The sensitivity indices of the phenological information in the AGB and PNC prediction models were 0.81–0.86 and 0.66–0.73, whereas LAI, LGB, LNC, and SPAD prediction models produced sensitivity indexes of 0.01–0.02, 0.01–0.02, 0.01–0.02, and 0.02–0.08, respectively.(3) The AGB and PNC prediction models considering ZS were more accurate than the prediction models based on VI. Whether or not phenological information is used, there was no difference in model accuracy for LGB,LNC, SPAD, and LAI. This study may provide a guideline for deciding whether phenological correction is required for estimation of agronomic traits across multiple growing seasons.

Farmers’precision pesticide technology adoption and its influencing factors:Evidence from apple production areas in China

The research aimed to understand farmers’willingness to adopt(WTA)and willingness to pay(WTP)for precision pesticide technologies and analyzed the determinants of farmers’decision-making.We used a two-stage approach to consider farmers’WTA and WTP for precision pesticide technologies.A survey of 545 apple farmers was administered in Bohai Bay and the Loess Plateau in China.The data were analyzed using the double-hurdle model.The results indicated that 78.72%of respondents were willing to apply precision pesticide technologies provided by service organizations such as cooperatives and dedicated enterprises,and 69.72%were willing to buy the equipment for using precision pesticide technologies.The results of the determinant analysis indicated that farmers’perceived perceptions,farm scale,cooperative membership,access to digital information,and availability of financial services had significant and positive impacts on farmers’WTA precision pesticide technologies.Cooperative membership,technical training,and adherence to environmental regulations increased farmers’WTP for precision pesticide technologies.Moreover,nonlinear relationships between age,agricultural experience,and farmers’WTA and WTP for precision pesticide technology services were found.

Estimating total leaf nitrogen concentration in winter wheat by canopy hyperspectral data and nitrogen vertical distribution

The use of remote sensing to monitor nitrogen(N) in crops is important for obtaining both economic benefit and ecological value because it helps to improve the efficiency of fertilization and reduces the ecological and environmental burden.In this study,we model the total leaf N concentration(TLNC) in winter wheat constructed from hyperspectral data by considering the vertical N distribution(VND).The field hyperspectral data of winter wheat acquired during the 2013–2014 growing season were used to construct and validate the model.The results show that:(1) the vertical distribution law of LNC was distinct,presenting a quadratic polynomial tendency from the top layer to the bottom layer.(2) The effective layer for remote sensing detection varied at different growth stages.The entire canopy,the three upper layers,the three upper layers,and the top layer are the effective layers at the jointing stage,flag leaf stage,flowering stages,and filling stage,respectively.(3) The TLNC model considering the VND has high predicting accuracy and stability.For models based on the greenness index(GI),mND705(modified normalized difference 705),and normalized difference vegetation index(NDVI),the values for the determining coefficient(R2),and normalized root mean square error(nRMSE) are 0.61 and 8.84%,0.59 and 8.89%,and 0.53 and 9.37%,respectively.Therefore,the LNC model with VND provides an accurate and non-destructive method to monitor N levels in the field.

Measurements and analysis of water content in winter wheat leaf based on terahertz spectroscopy

Wheat is a major grain crop in China.Water is one of the most important factors which influence the lifecycle and yield of wheat.It is of great significance to study the water content at the key stages of wheat growth in order to make irrigation decision to raise its yield.As Terahertz(THz)spectroscopy is a brand new sensing technology and sensitive to water absorption,the relationship between terahertz spectra and water content in winter wheat leaf was investigated and a preliminary result was presented in this paper.Forty winter wheat leaves samples with diverse range of water content(42.8%-72.5%)were collected.The Terahertz time domain spectra(THz-TDS)were first obtained and then transformed into Frequency-domain amplitude with the Fast Fourier Transformation(FFT)method.The absorption and refractive index spectra were then calculated.The spectra were linearly fitted to obtain the slope and intercept used for building a calibration model.The partial least squares(PLS)method and linear regression were employed to establish models to determine leaf water content in the winter wheat.The predicted correlation coefficient and the root mean square error of the optimal model established with the Frequency-domain amplitude parameter at 0.3 THz by linear regression were 0.812%and 4.4%,respectively.The results showed that terahertz spectroscopy performed well in water content prediction and could be an effective and potential method for leaf water content measurement in winter wheat.

Agri-BIGDATA:A smart pathway for crop nitrogen inputs

Big data provide a pathway to lower crop nitrogen inputs from genetic breeding to field production.Moreover,multidisciplinary efforts from plant health sensing,deep machine learning and cloud computing can integrate multi-source data to form information and knowledge.So big data analysis as a prospective optimal method,will make leaps towards addressing future issues of sustainable agriculture.