[R] lm without intercept

[Jan]

Hi,

I am not a statistics expert, so I have this question. A linear model
gives me the following summary:

Call:
lm(formula = N ~ N_alt)

Residuals:
    Min      1Q  Median      3Q     Max 
-110.30  -35.80  -22.77   38.07  122.76 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)  
(Intercept)  13.5177   229.0764   0.059   0.9535  
N_alt         0.2832     0.1501   1.886   0.0739 .
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 

Residual standard error: 56.77 on 20 degrees of freedom
  (16 observations deleted due to missingness)
Multiple R-squared: 0.151, Adjusted R-squared: 0.1086 
F-statistic: 3.558 on 1 and 20 DF,  p-value: 0.07386 

The regression is not very good (high p-value, low R-squared). 
The Pr value for the intercept seems to indicate that it is zero with a
very high probability (95.35%). So I repeat the regression forcing the
intercept to zero:

Call:
lm(formula = N ~ N_alt - 1)

Residuals:
    Min      1Q  Median      3Q     Max 
-110.11  -36.35  -22.13   38.59  123.23 

Coefficients:
      Estimate Std. Error t value Pr(>|t|)    
N_alt 0.292046   0.007742   37.72   <2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 

Residual standard error: 55.41 on 21 degrees of freedom
  (16 observations deleted due to missingness)
Multiple R-squared: 0.9855, Adjusted R-squared: 0.9848 
F-statistic:  1423 on 1 and 21 DF,  p-value: < 2.2e-16 

1. Is my interpretation correct?
2. Is it possible that just by forcing the intercept to become zero, a
bad regression becomes an extremely good one?
3. Why doesn't lm suggest a value of zero (or near zero) by itself if
the regression is so much better with it?

Please excuse my ignorance.

Jan Rheinländer