I ran this to get the intercepts and the r squared.

m0 <- lm(kg.ha ~ 1,LAIDat)
m1 <- lm(kg.ha ~ LAI, LAIDat)
summary(m1)
## 
## Call:
## lm(formula = kg.ha ~ LAI, data = LAIDat)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -840.54 -165.06    3.15  152.27  848.19 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   365.01      67.16   5.435 6.21e-07 ***
## LAI           366.73      26.86  13.653  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 303 on 77 degrees of freedom
## Multiple R-squared:  0.7077, Adjusted R-squared:  0.7039 
## F-statistic: 186.4 on 1 and 77 DF,  p-value: < 2.2e-16
anova(m0,m1)
## Analysis of Variance Table
## 
## Model 1: kg.ha ~ 1
## Model 2: kg.ha ~ LAI
##   Res.Df      RSS Df Sum of Sq      F    Pr(>F)    
## 1     78 24186403                                  
## 2     77  7070635  1  17115768 186.39 < 2.2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Val <- read.csv("C:/Users/micayla.lakey/Desktop/R/data/validpts.csv")
Val$kg.ha<-with(Val, biomass*4.325)

predbio.gg <- ggplot(data=Val, aes(x=LAI, y=366.73*LAI+365.01))+
  geom_point(color="orange") +
  theme_bw(20)+
  labs(x="Leaf Area Index (LAI)",
       y="Predicted Biomass")

predbio.gg +
  geom_smooth(method = "lm",se=FALSE, color="green") +
  ggtitle("Predicted Biomass of Validation Samples")

Val <- read.csv("C:/Users/micayla.lakey/Desktop/R/data/validpts.csv")
Val$kg.ha<-with(Val, biomass*4.325)

pvabio.gg <- ggplot(data=Val, aes(x=366.73*LAI+365.01, y=kg.ha))+
  geom_point(color="orange") +
  theme_bw(20)+
  labs(x="Predicted Biomass (kg/ha)",
       y="Actual Biomass (kg/ha)")

pvabio.gg +
  geom_smooth(method = "lm",se=FALSE, color="green") +
  ggtitle("Predicted Biomass vs. Actual biomass")+
  annotate("text", x=600, y=750, label="paste(R^2,\"=0.70\")",
           parse=TRUE, size=5)